OSM report 2022-2023
Marissa Dyck
2024-03-25
The first two chunks of this r markdown file after the r setup allow for plot zooming, but it also means that the html file must be opened in a browser to view the document properly. When it knits in RStudio the preview will appear empty but the html when opened in a browser will have all the info and you can click on each plot to Zoom in on it.
Before you begin
Notes
A few notes about this script.
If you are running this with the 2022-2023 data make sure you download the whole (OSM_2022-2023 GitHub repository)[https://github.com/ACMElabUvic/OSM_2022-2023] from the ACMElabUvic GitHub. This will ensure you have all the files, data, and proper folder structure you will need to run this code and associated analyses.
Also make sure you open RStudio through the R project (OSM_2022-2023.Rproj) this will automatically set your working directory to the correct place (wherever you saved the repository) and ensure you don’t have to change the file paths for some of the data.
Lastly, if you are looking to adapt this code for a future year of data, you will want to ensure you have run the ACME_camera_script_9-2-2024.R or .Rmd with your data as there is much data formatting, cleaning, and restructuring that has to be done before this code will work.
If you have question please email the most recent author, currently
Marissa A. Dyck
Postdoctoral research fellow
University of Victoria
School of Environmental Studies
Email: marissadyck17@gmail.com
Install packages
If you don’t already have the following packages installed, use the code below to install them.
install.packages('tidyverse')
install.packages('ggpubr')
install.packages('corrplot')
install.packages('Hmisc')
install.packages('glmmTMB')
install.packages('MuMIn')
install.packages('TMB', type = 'source')
install.packages('rphylopic')Load libraries
Then load the packages to your library.
library(tidyverse) # data tidying, visualization, and much more; this will load all tidyverse packages, can see complete list using tidyverse_packages()
library(ggpubr) # make modificaions to plot for publication (arrange plots)
library(PerformanceAnalytics) #Used to generate a correlation plot
library(Hmisc) # used to generate histograms for all variables in data frame
library(glmmTMB) #Constructing GLMMs## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.4.1
## Current Matrix version is 1.5.3
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' packagelibrary(MuMIn) # for model selection
library(rphylopic) # add animal silhouettes to graphsData
Load detection data
Read in saved and cleaned detection data from the ACME_camera_script_9-2-2024.R.
# detection data
# read in saved and cleaned detection data from the ACME_camera_script_9-2-2024.R
detections <- read_csv('data/processed/OSM_2022_ind_det.csv') %>%
# change site, species and event_id to factor
mutate_if(is.character,
as.factor)## Rows: 14102 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): array, site, species, event_id
## dbl (3): month, year, timediff
## dttm (1): datetime
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(detections)## tibble [14,102 × 8] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 4 levels "LU01","LU13",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 155 levels "LU01_06","LU01_10",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ species : Factor w/ 39 levels "ATVer","Beaver",..: 31 31 38 38 38 38 38 38 38 38 ...
## $ datetime: POSIXct[1:14102], format: "2022-06-17 10:01:52" "2023-09-10 12:51:15" ...
## $ month : num [1:14102] 6 9 6 7 7 7 8 8 8 8 ...
## $ year : num [1:14102] 2022 2023 2022 2022 2022 ...
## $ timediff: num [1:14102] NA 648166 NA 31847 21429 ...
## $ event_id: Factor w/ 14102 levels "E0","E1","E10",..: 1 2 5215 6326 7437 8548 9659 10770 11881 12992 ...Data formatting
In order to get plots that have the same formatting as last years’ report we have to do a bit of data formatting. First we need to make sure we are including the same relevant species (some were ignored for last years’ report or grouped together)
Last years report had the following species
- white-tailed deer
- snowshoe hare
- black bear
- coyote
- red squirrel
- fisher
- unknown
- moose
- lynx
- spruce grouse
- red fox
- striped skunk
- ruffed grouse
- owl
- grey wolf
- domestic dog
- cougar
- raven
- other
- mule deer
And they grouped all humans except for staff as ‘Humans’. Let’s look at the species we have in this year’s data and try to format it the same way
detections %>%
# group by array and species
group_by(species) %>%
summarise(n = n()) %>%
# have R print everything
print(n = nrow(.))## # A tibble: 39 × 2
## species n
## <fct> <int>
## 1 ATVer 32
## 2 Beaver 3
## 3 Black bear 1419
## 4 Canada goose 4
## 5 Caribou 70
## 6 Cougar 9
## 7 Coyote 990
## 8 Domestic dog 6
## 9 Fisher 187
## 10 Grey jay 50
## 11 Grey wolf 189
## 12 Human 5
## 13 Hunter 1
## 14 Long-tailed weasel 17
## 15 Lynx 364
## 16 Marten 140
## 17 Moose 617
## 18 Other 2
## 19 Other birds 175
## 20 Otter 7
## 21 Owl 12
## 22 Porcupine 5
## 23 Raven 6
## 24 Red fox 105
## 25 Red squirrel 1981
## 26 Ruffed grouse 43
## 27 Short-tailed weasel 21
## 28 Snowmobiler 7
## 29 Snowshoe hare 2911
## 30 Spruce grouse 77
## 31 Staff 302
## 32 Striped skunk 40
## 33 Unknown 556
## 34 Unknown canid 70
## 35 Unknown deer 292
## 36 Unknown mustelid 55
## 37 Unknown ungulate 17
## 38 White-tailed deer 3307
## 39 Wolverine 8Now let’s create a new data frame (tibble) to work with for the OSM figure summaries specifically
I personally would lump all the unknown together and all the birds together but for the sake of consistency with last year’s figures we will remove the same entries and keep the birds separate, let’s create a vector of entries to drop
species_drop <- c('Staff',
'Unknown deer',
'Unknown ungulate',
'Unknown canid',
'Unknown mustelid',
'Other birds')
# now we can create the new data frame with some changes consistent w/ choices made for 2021-2022
detections <- detections %>%
# for summarizing, lets lump all the recreational humans into "Humans"
mutate(species = recode_factor(species,
"Snowmobiler" = "Human",
"ATVer" = "Human",
'Hunter' = 'Human')) %>%
# remove species we don't want to plot
filter(!species %in% species_drop)
# look at data
str(detections)## tibble [13,191 × 8] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 4 levels "LU01","LU13",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 155 levels "LU01_06","LU01_10",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ species : Factor w/ 36 levels "Human","Beaver",..: 35 35 35 35 35 35 35 35 35 35 ...
## $ datetime: POSIXct[1:13191], format: "2022-06-18 11:09:19" "2022-07-10 13:56:10" ...
## $ month : num [1:13191] 6 7 7 7 8 8 8 8 10 10 ...
## $ year : num [1:13191] 2022 2022 2022 2022 2022 ...
## $ timediff: num [1:13191] NA 31847 21429 8356 1627 ...
## $ event_id: Factor w/ 14102 levels "E0","E1","E10",..: 5215 6326 7437 8548 9659 10770 11881 12992 3 1114 ...We will also want to subset the data by landscape unit (LU) and generate a new data frame for each LU to use for plotting
# we will also want to create a data frame for each LU to plot individually
# LU1
dets_LU1 <- detections %>%
filter(array == 'LU01')
# LU13
dets_LU13 <- detections %>%
filter(array == 'LU13')
# LU15
dets_LU15 <- detections %>%
filter(array == 'LU15')
# LU21
dets_LU21 <- detections %>%
filter(array == 'LU21')Marissa’s attempt at a for loop to subset data by array
We will also want to subset the data by landscape unit (LU) and generate a new data frame for each LU to use for plotting
Let’s see how this shit goes… probably bad but who knows
array_frames <- list()
for (i in unique(detections$array)){
#Subset data based on radius
df <- detections %>%
filter(array == i)
# list of dataframes
array_frames <- c(array_frames, list(df))
}
# inspect one data frame
print(array_frames[[1]]) # this is for LU01## # A tibble: 5,899 × 8
## array site species datetime month year timediff event_id
## <fct> <fct> <fct> <dttm> <dbl> <dbl> <dbl> <fct>
## 1 LU01 LU01_06 White-tailed… 2022-06-18 11:09:19 6 2022 NA E2
## 2 LU01 LU01_06 White-tailed… 2022-07-10 13:56:10 7 2022 31847. E3
## 3 LU01 LU01_06 White-tailed… 2022-07-25 11:04:44 7 2022 21429. E4
## 4 LU01 LU01_06 White-tailed… 2022-07-31 06:38:06 7 2022 8356. E5
## 5 LU01 LU01_06 White-tailed… 2022-08-01 09:45:28 8 2022 1627. E6
## 6 LU01 LU01_06 White-tailed… 2022-08-01 15:51:01 8 2022 364. E7
## 7 LU01 LU01_06 White-tailed… 2022-08-05 06:49:48 8 2022 5218. E8
## 8 LU01 LU01_06 White-tailed… 2022-08-26 08:36:07 8 2022 30345. E9
## 9 LU01 LU01_06 White-tailed… 2022-10-03 10:19:01 10 2022 54819. E10
## 10 LU01 LU01_06 White-tailed… 2022-10-03 15:36:14 10 2022 317. E11
## # ℹ 5,889 more rows… I think this worked
Now let’s change names of list items using purrr, couldn’t figure out how to name them in the loop, you don’t necessarily need to do this because we change the names in the next section, but I like having things named
array_frames <- array_frames %>%
purrr::set_names('LU01',
'LU13',
'LU15',
'LU21') #
# inspect each data frame
print(array_frames$LU01)## # A tibble: 5,899 × 8
## array site species datetime month year timediff event_id
## <fct> <fct> <fct> <dttm> <dbl> <dbl> <dbl> <fct>
## 1 LU01 LU01_06 White-tailed… 2022-06-18 11:09:19 6 2022 NA E2
## 2 LU01 LU01_06 White-tailed… 2022-07-10 13:56:10 7 2022 31847. E3
## 3 LU01 LU01_06 White-tailed… 2022-07-25 11:04:44 7 2022 21429. E4
## 4 LU01 LU01_06 White-tailed… 2022-07-31 06:38:06 7 2022 8356. E5
## 5 LU01 LU01_06 White-tailed… 2022-08-01 09:45:28 8 2022 1627. E6
## 6 LU01 LU01_06 White-tailed… 2022-08-01 15:51:01 8 2022 364. E7
## 7 LU01 LU01_06 White-tailed… 2022-08-05 06:49:48 8 2022 5218. E8
## 8 LU01 LU01_06 White-tailed… 2022-08-26 08:36:07 8 2022 30345. E9
## 9 LU01 LU01_06 White-tailed… 2022-10-03 10:19:01 10 2022 54819. E10
## 10 LU01 LU01_06 White-tailed… 2022-10-03 15:36:14 10 2022 317. E11
## # ℹ 5,889 more rowsDetection plots
Detection data
Now we can apply the same data formatting for each LUs’ data frame using purrr.
We want to count the number of independent detections per species per LU to use in the detection plots
# apply the same formatting to each LU data frame using purrr map
detection_data <- array_frames %>%
purrr::map(
~.x %>%
# group by species
group_by(species) %>%
# calculate a column with unique accounts of each species
mutate(count = n_distinct(event_id)) %>%
# keep just the columns we need
select(species, count) %>%
# keep only unique (distinct) rows so we should be left with one row per species, this helps with plotting later if you don't do it ggplot will try to count and plot each row it's annoying
distinct()) %>%
# set names of list objects
purrr::set_names('Detections LU01',
'Detections LU13',
'Detections LU15',
'Detections LU21')Detection plots
Now to graph independent detections for each LU using purrr, this avoids a TON of code repetition needed to plot each one individually
We use purrr::imap() instead of
purrr::map() because imap maintains the variable names in
our list (e.g. Detections LU01, Detections LU13, etc.) which we can then
use to title each plot.
Within purrr::imap() we just paste the code we would use
for a single ggplot since all the graphical elements (except the title
which we change with the file name [.y]) are the same
# create object detection plots which uses the detection_data list (w/ all 4 LUs)
detection_plots <- detection_data %>%
# use imap instead of map as it allows us to use .y to paste the list element names as the plot titles later
purrr::imap(
~.x %>%
# now just copy and paste the ggplot code for the detection graphs
ggplot(.,
aes(x = reorder(species, count), y = count)) +
# plot as bar graph using geom_col so we don't have to provide a y aesthetic
geom_col() +
# switch the x and y axis
coord_flip() +
# add the number of detections at the end of each bar
geom_text(aes(label = count),
color = "black",
size = 3,
hjust = -0.3,
vjust = 0.2) +
# label x and y axis with informative titles
labs(x = 'Species',
y = 'Number of Independent (30 min) Detections') +
# add title to plot with LU name the .y will take the name of whatever you named each list element in the detection_data list, so make sure this name is what you want on the ggtitle
ggtitle(.y) +
# set the theme
theme_classic() +
theme(plot.title = element_text(hjust = 0.5)))
# view plots, this will print each in it's own window so you have to scroll back in the plot viewer pane to look at each one
detection_plots## $`Detections LU01`
##
## $`Detections LU13`
##
## $`Detections LU15`
##
## $`Detections LU21`
Save detection plots
Now we want to save these plots in case we need each individual one (we will combine the detection and naive occ plots into a single figure for each LU later and use those for the OSM report, but we may want these standalone plots later so let’s save them while they are here).
We can save all the plots from the purrr iteration above using
purrr::imap. imap is used instead of map because it allows
us to retain the list object names (plot names) to paste as the file
name with the .y command.
IMPORTANT if you are using this code for a future github repo, DO NOT use .tiff as the file extension. This will cause issues when trying to push any changes to the github repo as the files are too large to meet githubs requirements
# save plots only use if needed
purrr::imap(
detection_plots,
~ggsave(.x,
file = paste0("figures/",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 11,
height = 9,
units = 'in'))Naive occupancy
Data
We also need to alter the detection data a bit to use for naive occupancy plots.
We will use the individual LU detection data like we did before and
use purrr::map() to apply the dame data formatting to all 4
data frames.
Here we want to calculate the total number of sites in each LU, the number of sites each species was detected at in each LU and then use both those numbers to calculate naive occupancy for each species in each LU
# First we need to alter the data frame a bit for these plots, let's create a data frame for each LU (I couldn't figure out how to do this without assigning individual data frames for each UGH)
# apply the same formatting to each data frame using purrr
occupancy_data <- array_frames %>%
purrr::map(
~.x %>%
# calculate the total number of sites for each LU
mutate(total_sites = n_distinct(site)) %>%
# group by species to calculate the number of sites each spp occurred at
group_by(species) %>%
# add columns to count the number of sites each spp occurred at and then the naive occupancy
reframe(count = n_distinct(site),
naive_occ = count/total_sites,
ind_det = n_distinct(event_id)) %>%
# keep just the columns we need
select(species, naive_occ, ind_det) %>%
# keep only unique (distinct) rows so we should be left with one row per species, this helps with plotting
distinct()) %>%
purrr::set_names('Naive Occupancy LU01',
'Naive Occupancy LU13',
'Naive Occupancy LU15',
'Naive Occupancy LU21')Occupancy plots
Now we can graph naive occupancy for each LU using purrr, and as with the detection plots this saves a massive amount of coding using purrr to run an iteration on the data files and produce four plots at once instead of copying and pasting code for each individually
# create object occupancy_plots which uses the occupancy_data list (w/ all 4 LUs)
occupancy_plots <- occupancy_data %>%
# use imap instead of map as it allows us to use .y to paste the list element names as the plot titles later
purrr::imap(
~.x %>%
# now just copy and paste the ggplot code for the occupancy graphs
ggplot(.,
aes(x = fct_reorder(species,
ind_det), # this reorders the species so they match the order of the detection plot which makes it better for viewing when the plots are arranged together in 1 figure for each LU
y = naive_occ)) +
# plot as bars using geom_col() which uses stat = 'identity', instead of geom_bar() which will count the rows in each group and plot that instead of naive occ
geom_col() +
# flip x and y axis
coord_flip() +
# add text to end of bars that provides naive occ value
geom_text(aes(label = round(naive_occ, 2)),
size = 3,
hjust = -0.3,
vjust = 0.2) +
# relabel x and y axis and title
labs(x = 'Species',
y = 'Proportion of Sites With At Least One Detection') +
# set plot title using .y (name of list object)
ggtitle(.y) +
# set. theme elements
theme_classic()+
theme(plot.title = element_text(hjust = 0.5)))
# view plots
occupancy_plots## $`Naive Occupancy LU01`
##
## $`Naive Occupancy LU13`
##
## $`Naive Occupancy LU15`
##
## $`Naive Occupancy LU21`
Save occupancy plots
As with the detection plots, we might want these individual plots
later for something so we can use purrr::imap() to save
them to the figures folder
Again avoid using the .tiff extension in github
# save plots
purrr::imap(
occupancy_plots,
~ggsave(.x,
file = paste0("figures/",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 11,
height = 9,
units = 'in'))Final combined plots for report
The previous year’s report had a figure for each LU with the
detections plot on the top and the occupancy plot on the bottom so we
will recreate these for this year using ggarrange().
Unfortunately I could not figure out how to do this in purrr to reduce coding but luckily it isn’t too much repitition
# not sure I know how to do the following section in purrr just yet, but we've saved a ton of coding so far and it doesn't take much to arrange each of these individually
# LU1
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU1_det_occ_plots <- ggarrange(detection_plots$`Detections LU01`, occupancy_plots$`Naive Occupancy LU01`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU1_det_occ_plots
# LU13
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU13_det_occ_plots <- ggarrange(detection_plots$`Detections LU13`, occupancy_plots$`Naive Occupancy LU13`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU13_det_occ_plots
# LU15
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU15_det_occ_plots <- ggarrange(detection_plots$`Detections LU15`, occupancy_plots$`Naive Occupancy LU15`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU15_det_occ_plots
# LU21
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU21_det_occ_plots <- ggarrange(detection_plots$`Detections LU21`, occupancy_plots$`Naive Occupancy LU21`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU21_det_occ_plots
We can however, save all the figures again using purrr
# save all figures at once using purrr
final_det_occ_plots <- list(LU1_det_occ_plots,
LU13_det_occ_plots,
LU15_det_occ_plots,
LU21_det_occ_plots) %>%
purrr::set_names('LU01_det_occ_plots',
'LU13_det_occ_plots',
'LU15_det_occ_plots',
'LU21_det_occ_plots') %>%
purrr::imap(
~ggsave(.x,
file = paste0("figures/",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 12,
height = 15,
units = 'in'))Analysis prep
Read in data
We need the proportional binomial data and the covariate data (from the ACME_camera_script_9-2-2024.R or .Rmd), let’s read those in now and check the structure of each
# response metric (proportional detections from the from the ACME_camera_script_9-2-2024.R or .Rmd)
prop_detections <- read_csv('data/processed/OSM_2022_proportional_detections.csv')## Rows: 154 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): site
## dbl (24): black_bear, coyote, fisher, moose, snowshoe_hare, white-tailed_dee...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# check variable structure
str(prop_detections)## spc_tbl_ [154 × 25] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ site : chr [1:154] "LU01_06" "LU01_10" "LU01_11" "LU01_13" ...
## $ black_bear : num [1:154] 7 3 4 7 8 9 4 5 7 7 ...
## $ coyote : num [1:154] 4 4 8 10 11 9 11 0 9 4 ...
## $ fisher : num [1:154] 5 3 3 3 2 1 1 1 0 3 ...
## $ moose : num [1:154] 3 2 5 9 1 0 2 4 1 0 ...
## $ snowshoe_hare : num [1:154] 4 1 3 0 8 2 2 0 12 4 ...
## $ white-tailed_deer : num [1:154] 12 5 12 12 13 14 15 9 12 10 ...
## $ cougar : num [1:154] 0 0 1 0 1 0 0 0 0 0 ...
## $ grey_wolf : num [1:154] 0 0 2 0 0 0 1 0 0 0 ...
## $ lynx : num [1:154] 0 0 1 0 1 1 0 0 0 2 ...
## $ red_fox : num [1:154] 0 0 2 0 0 0 0 0 4 0 ...
## $ wolverine : num [1:154] 0 0 0 0 0 0 0 0 0 0 ...
## $ caribou : num [1:154] 0 0 0 0 0 0 0 0 0 0 ...
## $ absent_black_bear : num [1:154] 5 3 8 5 4 3 8 7 5 5 ...
## $ absent_coyote : num [1:154] 10 1 6 5 3 5 4 15 6 11 ...
## $ absent_fisher : num [1:154] 9 2 11 12 12 13 14 14 15 12 ...
## $ absent_moose : num [1:154] 11 3 9 6 13 14 13 11 14 15 ...
## $ absent_snowshoe_hare : num [1:154] 10 4 11 15 6 12 13 15 3 11 ...
## $ absent_white-tailed_deer: num [1:154] 2 0 2 3 1 0 0 6 3 5 ...
## $ absent_cougar : num [1:154] 14 5 13 15 13 14 15 15 15 15 ...
## $ absent_grey_wolf : num [1:154] 14 5 12 15 14 14 14 15 15 15 ...
## $ absent_lynx : num [1:154] 14 5 13 15 13 13 15 15 15 13 ...
## $ absent_red_fox : num [1:154] 14 5 12 15 14 14 15 15 11 15 ...
## $ absent_wolverine : num [1:154] 14 5 14 15 14 14 15 15 15 15 ...
## $ absent_caribou : num [1:154] 14 5 14 15 14 14 15 15 15 15 ...
## - attr(*, "spec")=
## .. cols(
## .. site = col_character(),
## .. black_bear = col_double(),
## .. coyote = col_double(),
## .. fisher = col_double(),
## .. moose = col_double(),
## .. snowshoe_hare = col_double(),
## .. `white-tailed_deer` = col_double(),
## .. cougar = col_double(),
## .. grey_wolf = col_double(),
## .. lynx = col_double(),
## .. red_fox = col_double(),
## .. wolverine = col_double(),
## .. caribou = col_double(),
## .. absent_black_bear = col_double(),
## .. absent_coyote = col_double(),
## .. absent_fisher = col_double(),
## .. absent_moose = col_double(),
## .. absent_snowshoe_hare = col_double(),
## .. `absent_white-tailed_deer` = col_double(),
## .. absent_cougar = col_double(),
## .. absent_grey_wolf = col_double(),
## .. absent_lynx = col_double(),
## .. absent_red_fox = col_double(),
## .. absent_wolverine = col_double(),
## .. absent_caribou = col_double()
## .. )
## - attr(*, "problems")=<externalptr># model covariates (merged HFI and VEG data from the ACME_camera_script_9-2-2024.R or .Rmd)
covariates <- read_csv('data/processed/OSM_2022_covariates.csv',
# set the column types to read in correctly
col_types = cols(array = col_factor(),
camera = col_factor(),
site = col_factor(),
buff_dist = col_factor(),
.default = col_number()))
# check variable structure
str(covariates)## spc_tbl_ [3,100 × 119] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ array : Factor w/ 4 levels "LU13","LU15",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ camera : Factor w/ 96 levels "18","15","03",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ site : Factor w/ 155 levels "LU13_18","LU13_15",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ buff_dist : Factor w/ 20 levels "250","500","750",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ vegetated_edge_roads : num [1:3100] 0 0.0858 0 0 0 ...
## $ harvest_area : num [1:3100] 0 0 0.687 0.337 0 ...
## $ road_gravel_1l : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ conventional_seismic : num [1:3100] 0 0.03276 0 0.00889 0.01145 ...
## $ tame_pasture : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ pipeline : num [1:3100] 0 0.068 0 0 0.0301 ...
## $ road_gravel_2l : num [1:3100] 0 0 0 0 0 ...
## $ trail : num [1:3100] 0.00588 0.0028 0 0.00196 0 ...
## $ well_bitumen : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ rough_pasture : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ well_aband : num [1:3100] 0 0 0 0 0.0322 ...
## $ road_unclassified : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ crop : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ low_impact_seismic : num [1:3100] 0 0 0 0 0.0523 ...
## $ clearing_unknown : num [1:3100] 0.0923 0.0697 0 0 0 ...
## $ cultivation_abandoned : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ road_paved_undiv_2l : num [1:3100] 0 0.0174 0 0 0 ...
## $ road_unimproved : num [1:3100] 0 0 0 0 0 ...
## $ truck_trail : num [1:3100] 0 0 0 0.0139 0 ...
## $ dugout : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ road_paved_undiv_1l : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ well_gas : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ vegetated_edge_railways : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ harvest_area_white_zone : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ country_residence : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ borrowpit_dry : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ rural_residence : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ borrowpit_wet : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ borrowpits : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ grvl_sand_pit : num [1:3100] 0 0.0873 0 0 0 ...
## $ ris_reclaimed_temp : num [1:3100] 0 0.0477 0 0 0 ...
## $ ris_clearing_unknown : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_drainage : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_mines_oilsands : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_overburden_dump : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_facility_operations : num [1:3100] 0 0 0 0 0 ...
## $ transmission_line : num [1:3100] 0.0642 0 0 0 0.091 ...
## $ ris_tailing_pond : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ clearing_wellpad_unconfirmed: num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ mines_oilsands : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_soil_replaced : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ road_paved_1l : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_oilsands_rms : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_facility_unknown : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_borrowpits : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_transmission_line : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_soil_salvaged : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_road : num [1:3100] 0 0 0 0 0 ...
## $ ris_plant : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ urban_residence : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ facility_other : num [1:3100] 0 0 0 0 0 ...
## $ airp_runway : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ runway : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_reclaimed_permanent : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ urban_industrial : num [1:3100] 0.291 0 0 0 0 ...
## $ lagoon : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ facility_unknown : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ residence_clearing : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ well_cased : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ road_unpaved_2l : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ road_paved_3l : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ surrounding_veg : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ rlwy_sgl_track : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ road_winter : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ sump : num [1:3100] 0 0 0 0 0 ...
## $ greenspace : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ road_paved_2l : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ well_other : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ canal : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ reservoir : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ well_cleared_not_confirmed : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ misc_oil_gas_facility : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ camp_industrial : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_camp_industrial : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ oil_gas_plant : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ well_unknown : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_utilities : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ cfo : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ recreation : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ campground : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ peat : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ golfcourse : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ landfill : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ transfer_station : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ mill : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ road_paved_div : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ rlwy_spur : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ well_cleared_not_drilled : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ open_pit_mine : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ well_oil : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ road_paved_4l : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ mines_pitlake : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_reclaimed_certified : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ ris_windrow : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ tailing_pond : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## [list output truncated]
## - attr(*, "spec")=
## .. cols(
## .. .default = col_number(),
## .. array = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. camera = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. site = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. buff_dist = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. vegetated_edge_roads = col_number(),
## .. harvest_area = col_number(),
## .. road_gravel_1l = col_number(),
## .. conventional_seismic = col_number(),
## .. tame_pasture = col_number(),
## .. pipeline = col_number(),
## .. road_gravel_2l = col_number(),
## .. trail = col_number(),
## .. well_bitumen = col_number(),
## .. rough_pasture = col_number(),
## .. well_aband = col_number(),
## .. road_unclassified = col_number(),
## .. crop = col_number(),
## .. low_impact_seismic = col_number(),
## .. clearing_unknown = col_number(),
## .. cultivation_abandoned = col_number(),
## .. road_paved_undiv_2l = col_number(),
## .. road_unimproved = col_number(),
## .. truck_trail = col_number(),
## .. dugout = col_number(),
## .. road_paved_undiv_1l = col_number(),
## .. well_gas = col_number(),
## .. vegetated_edge_railways = col_number(),
## .. harvest_area_white_zone = col_number(),
## .. country_residence = col_number(),
## .. borrowpit_dry = col_number(),
## .. rural_residence = col_number(),
## .. borrowpit_wet = col_number(),
## .. borrowpits = col_number(),
## .. grvl_sand_pit = col_number(),
## .. ris_reclaimed_temp = col_number(),
## .. ris_clearing_unknown = col_number(),
## .. ris_drainage = col_number(),
## .. ris_mines_oilsands = col_number(),
## .. ris_overburden_dump = col_number(),
## .. ris_facility_operations = col_number(),
## .. transmission_line = col_number(),
## .. ris_tailing_pond = col_number(),
## .. clearing_wellpad_unconfirmed = col_number(),
## .. mines_oilsands = col_number(),
## .. ris_soil_replaced = col_number(),
## .. road_paved_1l = col_number(),
## .. ris_oilsands_rms = col_number(),
## .. ris_facility_unknown = col_number(),
## .. ris_borrowpits = col_number(),
## .. ris_transmission_line = col_number(),
## .. ris_soil_salvaged = col_number(),
## .. ris_road = col_number(),
## .. ris_plant = col_number(),
## .. urban_residence = col_number(),
## .. facility_other = col_number(),
## .. airp_runway = col_number(),
## .. runway = col_number(),
## .. ris_reclaimed_permanent = col_number(),
## .. urban_industrial = col_number(),
## .. lagoon = col_number(),
## .. facility_unknown = col_number(),
## .. residence_clearing = col_number(),
## .. well_cased = col_number(),
## .. road_unpaved_2l = col_number(),
## .. road_paved_3l = col_number(),
## .. surrounding_veg = col_number(),
## .. rlwy_sgl_track = col_number(),
## .. road_winter = col_number(),
## .. sump = col_number(),
## .. greenspace = col_number(),
## .. road_paved_2l = col_number(),
## .. well_other = col_number(),
## .. canal = col_number(),
## .. reservoir = col_number(),
## .. well_cleared_not_confirmed = col_number(),
## .. misc_oil_gas_facility = col_number(),
## .. camp_industrial = col_number(),
## .. ris_camp_industrial = col_number(),
## .. oil_gas_plant = col_number(),
## .. well_unknown = col_number(),
## .. ris_utilities = col_number(),
## .. cfo = col_number(),
## .. recreation = col_number(),
## .. campground = col_number(),
## .. peat = col_number(),
## .. golfcourse = col_number(),
## .. landfill = col_number(),
## .. transfer_station = col_number(),
## .. mill = col_number(),
## .. road_paved_div = col_number(),
## .. rlwy_spur = col_number(),
## .. well_cleared_not_drilled = col_number(),
## .. open_pit_mine = col_number(),
## .. well_oil = col_number(),
## .. road_paved_4l = col_number(),
## .. mines_pitlake = col_number(),
## .. ris_reclaimed_certified = col_number(),
## .. ris_windrow = col_number(),
## .. tailing_pond = col_number(),
## .. rlwy_mlt_track = col_number(),
## .. rlwy_dbl_track = col_number(),
## .. ris_waste = col_number(),
## .. interchange_ramp = col_number(),
## .. road_paved_5l = col_number(),
## .. ris_airp_runway = col_number(),
## .. fruit_vegetables = col_number(),
## .. road_unpaved_1l = col_number(),
## .. ris_reclaim_ready = col_number(),
## .. ris_tank_farm = col_number(),
## .. lc_class20 = col_number(),
## .. lc_class32 = col_number(),
## .. lc_class33 = col_number(),
## .. lc_class34 = col_number(),
## .. lc_class50 = col_number(),
## .. lc_class110 = col_number(),
## .. lc_class120 = col_number(),
## .. lc_class210 = col_number(),
## .. lc_class220 = col_number(),
## .. lc_class230 = col_number()
## .. )
## - attr(*, "problems")=<externalptr>Format covariates
There are too many covariates to include in the models individually and many of them describe similar HFI features. We can use the info from the README file in this repository which includes detailed descriptions from the ABMI human footprints wall to wall data download website for Year 2021 OR in the relevant_literature folder of this repository (HFI_2021_v1_0_Metadata_Final.pdf).
the current version of this code for the purposes of the 2022-2023 report used a merged dataset from 2021-2022 and 2022-2023 data, howver each year of data the variables were extracted slightly differenty from GIS so final version of this code will include a different formatting process which will likely occur in the ACME_camera_script_9-2-2024.R or .Rmd’
First lets order the columns alphabetically so we can look at descriptions for everything in the ABMI doc easier. We will want the non-covariate columns (i.e., array, site, camera, buffer_dsit) at the front so we can use relocate after we order all of the columns to move these four to the front of the data.
covariates <- covariates %>%
# order columns alphabetically
select(order(colnames(.))) %>%
# we want to move the columns that aren't HFI features or landcover to the front
relocate(.,
c(array,
site,
camera,
buff_dist))
# get a list of column names to ensure it worked
names(covariates)## [1] "array" "site"
## [3] "camera" "buff_dist"
## [5] "airp_runway" "borrowpit_dry"
## [7] "borrowpit_wet" "borrowpits"
## [9] "camp_industrial" "campground"
## [11] "canal" "cfo"
## [13] "clearing_unknown" "clearing_wellpad_unconfirmed"
## [15] "conventional_seismic" "country_residence"
## [17] "crop" "cultivation_abandoned"
## [19] "dugout" "facility_other"
## [21] "facility_unknown" "fruit_vegetables"
## [23] "golfcourse" "greenspace"
## [25] "grvl_sand_pit" "harvest_area"
## [27] "harvest_area_white_zone" "interchange_ramp"
## [29] "lagoon" "landfill"
## [31] "lc_class110" "lc_class120"
## [33] "lc_class20" "lc_class210"
## [35] "lc_class220" "lc_class230"
## [37] "lc_class32" "lc_class33"
## [39] "lc_class34" "lc_class50"
## [41] "low_impact_seismic" "mill"
## [43] "mines_oilsands" "mines_pitlake"
## [45] "misc_oil_gas_facility" "oil_gas_plant"
## [47] "open_pit_mine" "peat"
## [49] "pipeline" "recreation"
## [51] "reservoir" "residence_clearing"
## [53] "ris_airp_runway" "ris_borrowpits"
## [55] "ris_camp_industrial" "ris_clearing_unknown"
## [57] "ris_drainage" "ris_facility_operations"
## [59] "ris_facility_unknown" "ris_mines_oilsands"
## [61] "ris_oilsands_rms" "ris_overburden_dump"
## [63] "ris_plant" "ris_reclaim_ready"
## [65] "ris_reclaimed_certified" "ris_reclaimed_permanent"
## [67] "ris_reclaimed_temp" "ris_road"
## [69] "ris_soil_replaced" "ris_soil_salvaged"
## [71] "ris_tailing_pond" "ris_tank_farm"
## [73] "ris_transmission_line" "ris_utilities"
## [75] "ris_waste" "ris_windrow"
## [77] "rlwy_dbl_track" "rlwy_mlt_track"
## [79] "rlwy_sgl_track" "rlwy_spur"
## [81] "road_gravel_1l" "road_gravel_2l"
## [83] "road_paved_1l" "road_paved_2l"
## [85] "road_paved_3l" "road_paved_4l"
## [87] "road_paved_5l" "road_paved_div"
## [89] "road_paved_undiv_1l" "road_paved_undiv_2l"
## [91] "road_unclassified" "road_unimproved"
## [93] "road_unpaved_1l" "road_unpaved_2l"
## [95] "road_winter" "rough_pasture"
## [97] "runway" "rural_residence"
## [99] "sump" "surrounding_veg"
## [101] "tailing_pond" "tame_pasture"
## [103] "trail" "transfer_station"
## [105] "transmission_line" "truck_trail"
## [107] "urban_industrial" "urban_residence"
## [109] "vegetated_edge_railways" "vegetated_edge_roads"
## [111] "well_aband" "well_bitumen"
## [113] "well_cased" "well_cleared_not_confirmed"
## [115] "well_cleared_not_drilled" "well_gas"
## [117] "well_oil" "well_other"
## [119] "well_unknown"Let’s get a summary of each variable now, and lets filter by just the 1000m buffer width so we don’t have a bunch of repeated data for each buffer width at each site, this will give us general insights into how much variability we have with each feature at a general buffer width. *You can change this if you are interested in a different bufffer width specifically, or if it makes more since to see the data for the min (250m) or max (5000m) buffer width.
covariates %>%
# filter to just buffer 1000 m
filter(buff_dist == 1000) %>%
summary(.)## array site camera buff_dist airp_runway
## LU13:41 LU13_18: 1 27 : 4 1000 :155 Min. :0
## LU15:39 LU13_15: 1 32 : 4 250 : 0 1st Qu.:0
## LU21:36 LU13_03: 1 41 : 4 500 : 0 Median :0
## LU01:39 LU13_34: 1 36 : 4 750 : 0 Mean :0
## LU13_57: 1 16 : 3 1250 : 0 3rd Qu.:0
## LU13_16: 1 21 : 3 1500 : 0 Max. :0
## (Other):149 (Other):133 (Other): 0
## borrowpit_dry borrowpit_wet borrowpits
## Min. :0.0000000 Min. :0.0000000 Min. :0.0000000
## 1st Qu.:0.0000000 1st Qu.:0.0000000 1st Qu.:0.0000000
## Median :0.0000000 Median :0.0000000 Median :0.0000000
## Mean :0.0009388 Mean :0.0006446 Mean :0.0002542
## 3rd Qu.:0.0000000 3rd Qu.:0.0000000 3rd Qu.:0.0000000
## Max. :0.0300351 Max. :0.0198622 Max. :0.0072821
##
## camp_industrial campground canal cfo clearing_unknown
## Min. :0.0000000 Min. :0 Min. :0 Min. :0 Min. :0.000000
## 1st Qu.:0.0000000 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0.000000
## Median :0.0000000 Median :0 Median :0 Median :0 Median :0.000000
## Mean :0.0003785 Mean :0 Mean :0 Mean :0 Mean :0.006422
## 3rd Qu.:0.0000000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.001654
## Max. :0.0160772 Max. :0 Max. :0 Max. :0 Max. :0.182912
##
## clearing_wellpad_unconfirmed conventional_seismic country_residence
## Min. :0.0000000 Min. :0.000000 Min. :0.0000000
## 1st Qu.:0.0000000 1st Qu.:0.002498 1st Qu.:0.0000000
## Median :0.0000000 Median :0.005202 Median :0.0000000
## Mean :0.0004428 Mean :0.006143 Mean :0.0000828
## 3rd Qu.:0.0000000 3rd Qu.:0.009577 3rd Qu.:0.0000000
## Max. :0.0117571 Max. :0.020381 Max. :0.0128340
##
## crop cultivation_abandoned dugout facility_other
## Min. :0 Min. :0.000e+00 Min. :0 Min. :0.000000
## 1st Qu.:0 1st Qu.:0.000e+00 1st Qu.:0 1st Qu.:0.000000
## Median :0 Median :0.000e+00 Median :0 Median :0.000000
## Mean :0 Mean :5.408e-05 Mean :0 Mean :0.001119
## 3rd Qu.:0 3rd Qu.:0.000e+00 3rd Qu.:0 3rd Qu.:0.000000
## Max. :0 Max. :8.383e-03 Max. :0 Max. :0.062266
##
## facility_unknown fruit_vegetables golfcourse greenspace
## Min. :0.000e+00 Min. :0 Min. :0 Min. :0
## 1st Qu.:0.000e+00 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0.000e+00 Median :0 Median :0 Median :0
## Mean :5.746e-05 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0.000e+00 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :3.281e-03 Max. :0 Max. :0 Max. :0
##
## grvl_sand_pit harvest_area harvest_area_white_zone interchange_ramp
## Min. :0.000000 Min. :0.00000 Min. :0 Min. :0
## 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0
## Median :0.000000 Median :0.00000 Median :0 Median :0
## Mean :0.003109 Mean :0.02293 Mean :0 Mean :0
## 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0 3rd Qu.:0
## Max. :0.109732 Max. :0.42899 Max. :0 Max. :0
##
## lagoon landfill lc_class110 lc_class120
## Min. :0.0000000 Min. :0 Min. :0.000000 Min. :0.000e+00
## 1st Qu.:0.0000000 1st Qu.:0 1st Qu.:0.004449 1st Qu.:0.000e+00
## Median :0.0000000 Median :0 Median :0.046414 Median :0.000e+00
## Mean :0.0002406 Mean :0 Mean :0.054946 Mean :3.878e-06
## 3rd Qu.:0.0000000 3rd Qu.:0 3rd Qu.:0.082004 3rd Qu.:0.000e+00
## Max. :0.0126573 Max. :0 Max. :0.231159 Max. :6.011e-04
##
## lc_class20 lc_class210 lc_class220 lc_class230
## Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.4659 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.7228 Median :0.01425 Median :0.02315
## Mean :0.02123 Mean :0.6400 Mean :0.10735 Mean :0.06363
## 3rd Qu.:0.00000 3rd Qu.:0.8433 3rd Qu.:0.16066 3rd Qu.:0.08982
## Max. :0.38025 Max. :0.9858 Max. :0.84274 Max. :0.47473
##
## lc_class32 lc_class33 lc_class34 lc_class50
## Min. :0 Min. :0.000000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.01205
## Median :0 Median :0.000000 Median :0.00000 Median :0.03874
## Mean :0 Mean :0.004366 Mean :0.03862 Mean :0.06991
## 3rd Qu.:0 3rd Qu.:0.000000 3rd Qu.:0.05870 3rd Qu.:0.09848
## Max. :0 Max. :0.243332 Max. :0.25234 Max. :0.55986
##
## low_impact_seismic mill mines_oilsands mines_pitlake
## Min. :0.000000 Min. :0 Min. :0 Min. :0
## 1st Qu.:0.000000 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0.000000 Median :0 Median :0 Median :0
## Mean :0.004172 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0.000063 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0.060391 Max. :0 Max. :0 Max. :0
##
## misc_oil_gas_facility oil_gas_plant open_pit_mine peat
## Min. :0.000000 Min. :0.000000 Min. :0.0000000 Min. :0
## 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.0000000 1st Qu.:0
## Median :0.000000 Median :0.000000 Median :0.0000000 Median :0
## Mean :0.002912 Mean :0.001167 Mean :0.0005665 Mean :0
## 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.0000000 3rd Qu.:0
## Max. :0.107208 Max. :0.071271 Max. :0.0389603 Max. :0
##
## pipeline recreation reservoir residence_clearing
## Min. :0.00000 Min. :0 Min. :0.000e+00 Min. :0
## 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0.000e+00 1st Qu.:0
## Median :0.02243 Median :0 Median :0.000e+00 Median :0
## Mean :0.02699 Mean :0 Mean :2.865e-05 Mean :0
## 3rd Qu.:0.03776 3rd Qu.:0 3rd Qu.:0.000e+00 3rd Qu.:0
## Max. :0.12204 Max. :0 Max. :4.441e-03 Max. :0
##
## ris_airp_runway ris_borrowpits ris_camp_industrial ris_clearing_unknown
## Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0
##
## ris_drainage ris_facility_operations ris_facility_unknown ris_mines_oilsands
## Min. :0 Min. :0.0000000 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0.0000000 1st Qu.:0 1st Qu.:0
## Median :0 Median :0.0000000 Median :0 Median :0
## Mean :0 Mean :0.0003528 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0.0000000 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0.0546781 Max. :0 Max. :0
##
## ris_oilsands_rms ris_overburden_dump ris_plant ris_reclaim_ready
## Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0
##
## ris_reclaimed_certified ris_reclaimed_permanent ris_reclaimed_temp
## Min. :0 Min. :0.0000000 Min. :0.000000
## 1st Qu.:0 1st Qu.:0.0000000 1st Qu.:0.000000
## Median :0 Median :0.0000000 Median :0.000000
## Mean :0 Mean :0.0002803 Mean :0.000318
## 3rd Qu.:0 3rd Qu.:0.0000000 3rd Qu.:0.000000
## Max. :0 Max. :0.0434483 Max. :0.016762
##
## ris_road ris_soil_replaced ris_soil_salvaged ris_tailing_pond
## Min. :0.0000000 Min. :0 Min. :0 Min. :0.0000000
## 1st Qu.:0.0000000 1st Qu.:0 1st Qu.:0 1st Qu.:0.0000000
## Median :0.0000000 Median :0 Median :0 Median :0.0000000
## Mean :0.0000302 Mean :0 Mean :0 Mean :0.0009116
## 3rd Qu.:0.0000000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.0000000
## Max. :0.0046809 Max. :0 Max. :0 Max. :0.1413014
##
## ris_tank_farm ris_transmission_line ris_utilities ris_waste ris_windrow
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0
##
## rlwy_dbl_track rlwy_mlt_track rlwy_sgl_track rlwy_spur road_gravel_1l
## Min. :0 Min. :0 Min. :0 Min. :0 Min. :0.000000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0.000000
## Median :0 Median :0 Median :0 Median :0 Median :0.004254
## Mean :0 Mean :0 Mean :0 Mean :0 Mean :0.004548
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.007252
## Max. :0 Max. :0 Max. :0 Max. :0 Max. :0.022773
##
## road_gravel_2l road_paved_1l road_paved_2l road_paved_3l road_paved_4l
## Min. :0.000000 Min. :0 Min. :0 Min. :0 Min. :0
## 1st Qu.:0.000000 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0.000000 Median :0 Median :0 Median :0 Median :0
## Mean :0.001748 Mean :0 Mean :0 Mean :0 Mean :0
## 3rd Qu.:0.000000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0.015867 Max. :0 Max. :0 Max. :0 Max. :0
##
## road_paved_5l road_paved_div road_paved_undiv_1l road_paved_undiv_2l
## Min. :0 Min. :0 Min. :0.0000000 Min. :0.0000000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.0000000 1st Qu.:0.0000000
## Median :0 Median :0 Median :0.0000000 Median :0.0000000
## Mean :0 Mean :0 Mean :0.0001162 Mean :0.0005722
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.0000000 3rd Qu.:0.0000000
## Max. :0 Max. :0 Max. :0.0085401 Max. :0.0118399
##
## road_unclassified road_unimproved road_unpaved_1l road_unpaved_2l
## Min. :0.00e+00 Min. :0.000000 Min. :0 Min. :0
## 1st Qu.:0.00e+00 1st Qu.:0.000000 1st Qu.:0 1st Qu.:0
## Median :0.00e+00 Median :0.000000 Median :0 Median :0
## Mean :2.20e-06 Mean :0.001069 Mean :0 Mean :0
## 3rd Qu.:0.00e+00 3rd Qu.:0.001017 3rd Qu.:0 3rd Qu.:0
## Max. :3.41e-04 Max. :0.010709 Max. :0 Max. :0
##
## road_winter rough_pasture runway rural_residence
## Min. :0 Min. :0.0000000 Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:0 1st Qu.:0.0000000 1st Qu.:0.000e+00 1st Qu.:0.000e+00
## Median :0 Median :0.0000000 Median :0.000e+00 Median :0.000e+00
## Mean :0 Mean :0.0001776 Mean :9.358e-05 Mean :5.795e-06
## 3rd Qu.:0 3rd Qu.:0.0000000 3rd Qu.:0.000e+00 3rd Qu.:0.000e+00
## Max. :0 Max. :0.0149983 Max. :1.451e-02 Max. :8.982e-04
##
## sump surrounding_veg tailing_pond tame_pasture
## Min. :0.000000 Min. :0 Min. :0 Min. :0.000e+00
## 1st Qu.:0.000000 1st Qu.:0 1st Qu.:0 1st Qu.:0.000e+00
## Median :0.000000 Median :0 Median :0 Median :0.000e+00
## Mean :0.003364 Mean :0 Mean :0 Mean :4.727e-06
## 3rd Qu.:0.002012 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.000e+00
## Max. :0.033997 Max. :0 Max. :0 Max. :7.326e-04
##
## trail transfer_station transmission_line truck_trail
## Min. :0.0000000 Min. :0 Min. :0.000000 Min. :0.0000000
## 1st Qu.:0.0000000 1st Qu.:0 1st Qu.:0.000000 1st Qu.:0.0000000
## Median :0.0001657 Median :0 Median :0.000000 Median :0.0000000
## Mean :0.0009478 Mean :0 Mean :0.007669 Mean :0.0008284
## 3rd Qu.:0.0015165 3rd Qu.:0 3rd Qu.:0.000000 3rd Qu.:0.0000000
## Max. :0.0068343 Max. :0 Max. :0.070051 Max. :0.0149490
##
## urban_industrial urban_residence vegetated_edge_railways
## Min. :0.000000 Min. :0 Min. :0
## 1st Qu.:0.000000 1st Qu.:0 1st Qu.:0
## Median :0.000000 Median :0 Median :0
## Mean :0.002782 Mean :0 Mean :0
## 3rd Qu.:0.000000 3rd Qu.:0 3rd Qu.:0
## Max. :0.215891 Max. :0 Max. :0
##
## vegetated_edge_roads well_aband well_bitumen well_cased
## Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0.0000000
## 1st Qu.:0.00379 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.0000000
## Median :0.01016 Median :0.001888 Median :0.000000 Median :0.0000000
## Mean :0.01569 Mean :0.004932 Mean :0.009243 Mean :0.0001615
## 3rd Qu.:0.02866 3rd Qu.:0.007000 3rd Qu.:0.012968 3rd Qu.:0.0000000
## Max. :0.06275 Max. :0.042874 Max. :0.083850 Max. :0.0071111
##
## well_cleared_not_confirmed well_cleared_not_drilled well_gas
## Min. :0.0000000 Min. :0 Min. :0.000e+00
## 1st Qu.:0.0000000 1st Qu.:0 1st Qu.:0.000e+00
## Median :0.0000000 Median :0 Median :0.000e+00
## Mean :0.0006285 Mean :0 Mean :8.574e-05
## 3rd Qu.:0.0000000 3rd Qu.:0 3rd Qu.:0.000e+00
## Max. :0.0365581 Max. :0 Max. :2.579e-03
##
## well_oil well_other well_unknown
## Min. :0 Min. :0.000000 Min. :0
## 1st Qu.:0 1st Qu.:0.000000 1st Qu.:0
## Median :0 Median :0.000000 Median :0
## Mean :0 Mean :0.001517 Mean :0
## 3rd Qu.:0 3rd Qu.:0.000000 3rd Qu.:0
## Max. :0 Max. :0.030134 Max. :0
## Let’s also plot histograms of each variable for data visualization in a for loop, I wanted to do this for just one buffer size to reduce replicates but it will also drop any variables for which all the data are zeros, so you could explore this at different buffer widths or just remove the filter function and look at all the data which is what I do below once it is grouped
# filter to just one buffer width
covariates_1000 <- covariates %>%
filter(buff_dist == 1000)
for (col in 1:ncol(covariates_1000)) {
hist(covariates_1000[,col])
}
Now we can use the information from the previous few steps as well as the variable descriptions from the ABMI human footprints wall to wall data download website for Year 2021 which is stored in the ‘relevant literature’ portion of this document AND also copied into the README file, to group the covariates so we reduce the number of potential variables to explore in the modeling phase.
We will use the mutate() function with some tidyverse
trickery (i.e., nesting across() and
contains() in rowsums()) to sum across each
observation (row) by searching for various character strings. If there
isn’t a common character string for multiple variables we want to sum
then we provide each one individually. We can also combine these methods
(e.g., with ‘facilities’ [see code]).
covariates_grouped <- covariates %>%
# rename 'vegetated_edge_roads so that we can use road as keyword to group roads without including this feature
rename('vegetated_edge_rds' = vegetated_edge_roads) %>%
# within the mutate function create new column names for the grouped variables
mutate(
# borrowpits
borrowpits = rowSums(across(contains('borrowpit'))) + # here we use rowsums with across() and contains() to sum acrross each row any values for columns that contain the keyword above. Be careful when using that there aren't any variables that match the string (keyword) provided that you don't want to include!
dugout +
lagoon +
sump,
# clearings
clearings = rowSums(across(contains('clearing'))) +
runway,
# cultivations
cultivation = crop +
cultivation_abandoned +
fruit_vegetables +
rough_pasture +
tame_pasture,
# harvest areas
harvest = rowSums(across(contains('harvest'))),
# industrial facilities
facilities = rowSums(across(contains('facility'))) +
rowSums(across(contains('plant'))) +
camp_industrial +
mill +
ris_camp_industrial +
ris_tank_farm +
ris_utilities +
urban_industrial,
# mine areas
mines = rowSums(across(contains('mine'))) +
rowSums(across(contains('tailing'))) +
grvl_sand_pit +
peat +
ris_drainage +
ris_oilsands_rms +
ris_overburden_dump +
ris_reclaim_ready +
ris_soil_salvaged +
ris_waste,
# railways
railways = rowSums(across(contains('rlwy'))),
# reclaimed areas
reclaimed = rowSums(across(contains('reclaimed'))) +
ris_soil_replaced +
ris_windrow,
# recreation areas
recreation = campground +
golfcourse +
greenspace +
recreation,
# residential areas (can't use residence as keyword because 'residence_clearing' is in clearing unless we rearrange groupings or rename that one)
residential = country_residence +
rural_residence +
urban_residence,
# roads (we renamed 'vegetated_edge_roads' above to 'vegetated_edge_rds' so we can use roads as keyword here which saves a bunch of coding as there are many many road variables)
roads = rowSums(across(contains('road'))) +
interchange_ramp +
airp_runway +
ris_airp_runway,
# seismic lines
seismic_lines = rowSums(across(contains('seismic'))),
# transmission lines
transmission_lines = rowSums(across(contains('transmission'))),
# trails
trails = rowSums(across(contains('trail'))),
# vegetated edges
veg_edges = rowSums(across(contains('vegetated'))) +
surrounding_veg,
# man-made water features
water = canal +
reservoir,
# well sites (this probably includes 'clearing_wellpad' need to check)
wells = rowSums(across(contains('well'))),
# remove columns that were used to create new columns to tidy the data frame
.keep = 'unused') %>%
# reorder variables so the veg data is after all the HFI data
relocate(starts_with('lc_class'),
.after = wells)
# see what's left
names(covariates_grouped)## [1] "array" "site" "camera"
## [4] "buff_dist" "borrowpits" "cfo"
## [7] "landfill" "pipeline" "recreation"
## [10] "transfer_station" "clearings" "cultivation"
## [13] "harvest" "facilities" "mines"
## [16] "railways" "reclaimed" "residential"
## [19] "roads" "seismic_lines" "transmission_lines"
## [22] "trails" "veg_edges" "water"
## [25] "wells" "lc_class110" "lc_class120"
## [28] "lc_class20" "lc_class210" "lc_class220"
## [31] "lc_class230" "lc_class32" "lc_class33"
## [34] "lc_class34" "lc_class50"# check the structure of new data
str(covariates_grouped)## tibble [3,100 × 35] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 4 levels "LU13","LU15",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 155 levels "LU13_18","LU13_15",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ camera : Factor w/ 96 levels "18","15","03",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ buff_dist : Factor w/ 20 levels "250","500","750",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ borrowpits : num [1:3100] 0 0 0 0 0 ...
## $ cfo : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ landfill : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ pipeline : num [1:3100] 0 0.068 0 0 0.0301 ...
## $ recreation : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ transfer_station : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ clearings : num [1:3100] 0.0923 0.0697 0 0 0 ...
## $ cultivation : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ harvest : num [1:3100] 0 0 0.687 0.337 0 ...
## $ facilities : num [1:3100] 0.291 0 0 0 0 ...
## $ mines : num [1:3100] 0 0.0873 0 0 0 ...
## $ railways : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ reclaimed : num [1:3100] 0 0.0477 0 0 0 ...
## $ residential : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ roads : num [1:3100] 0 0.0174 0 0 0 ...
## $ seismic_lines : num [1:3100] 0 0.03276 0 0.00889 0.06377 ...
## $ transmission_lines: num [1:3100] 0.0642 0 0 0 0.091 ...
## $ trails : num [1:3100] 0.00588 0.0028 0 0.01591 0 ...
## $ veg_edges : num [1:3100] 0 0.0858 0 0 0 ...
## $ water : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ wells : num [1:3100] 0 0 0 0 0.0322 ...
## $ lc_class110 : num [1:3100] 0.193 0.348 0 0 0.178 ...
## $ lc_class120 : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ lc_class20 : num [1:3100] 0.0361 0 0 0 0 ...
## $ lc_class210 : num [1:3100] 0.456 0.358 0.186 1 0.822 ...
## $ lc_class220 : num [1:3100] 0 0 0 0 0 ...
## $ lc_class230 : num [1:3100] 0 0.101 0.255 0 0 ...
## $ lc_class32 : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ lc_class33 : num [1:3100] 0 0.101 0 0 0 ...
## $ lc_class34 : num [1:3100] 0 0.0916 0 0 0 ...
## $ lc_class50 : num [1:3100] 0.316 0 0.559 0 0 ...# check summary of new data
summary(covariates_grouped)## array site camera buff_dist borrowpits
## LU13:820 LU13_18: 20 27 : 80 250 : 155 Min. :0.000000
## LU15:780 LU13_15: 20 32 : 80 500 : 155 1st Qu.:0.000000
## LU21:720 LU13_03: 20 41 : 80 750 : 155 Median :0.001649
## LU01:780 LU13_34: 20 36 : 80 1000 : 155 Mean :0.004302
## LU13_57: 20 16 : 60 1250 : 155 3rd Qu.:0.004453
## LU13_16: 20 21 : 60 1500 : 155 Max. :0.310957
## (Other):2980 (Other):2660 (Other):2170
## cfo landfill pipeline recreation
## Min. :0.000e+00 Min. :0 Min. :0.00000 Min. :0.000e+00
## 1st Qu.:0.000e+00 1st Qu.:0 1st Qu.:0.00000 1st Qu.:0.000e+00
## Median :0.000e+00 Median :0 Median :0.01350 Median :0.000e+00
## Mean :8.077e-07 Mean :0 Mean :0.01937 Mean :4.904e-05
## 3rd Qu.:0.000e+00 3rd Qu.:0 3rd Qu.:0.02812 3rd Qu.:0.000e+00
## Max. :1.215e-03 Max. :0 Max. :0.28897 Max. :1.337e-02
##
## transfer_station clearings cultivation harvest
## Min. :0 Min. :0.0000000 Min. :0.0000000 Min. :0.00000
## 1st Qu.:0 1st Qu.:0.0000000 1st Qu.:0.0000000 1st Qu.:0.00000
## Median :0 Median :0.0005278 Median :0.0000000 Median :0.00000
## Mean :0 Mean :0.0060419 Mean :0.0009397 Mean :0.01868
## 3rd Qu.:0 3rd Qu.:0.0040539 3rd Qu.:0.0000000 3rd Qu.:0.01348
## Max. :0 Max. :0.4024400 Max. :0.1253361 Max. :0.83674
##
## facilities mines railways reclaimed
## Min. :0.000000 Min. :0.000000 Min. :0 Min. :0.000000
## 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0 1st Qu.:0.000000
## Median :0.000000 Median :0.000000 Median :0 Median :0.000000
## Mean :0.006653 Mean :0.005448 Mean :0 Mean :0.001002
## 3rd Qu.:0.002769 3rd Qu.:0.000000 3rd Qu.:0 3rd Qu.:0.000000
## Max. :0.335753 Max. :0.557884 Max. :0 Max. :0.078321
##
## residential roads seismic_lines transmission_lines
## Min. :0.0000000 Min. :0.000000 Min. :0.000000 Min. :0.000000
## 1st Qu.:0.0000000 1st Qu.:0.001040 1st Qu.:0.003046 1st Qu.:0.000000
## Median :0.0000000 Median :0.004019 Median :0.008356 Median :0.000000
## Mean :0.0001473 Mean :0.006218 Mean :0.011034 Mean :0.005597
## 3rd Qu.:0.0000000 3rd Qu.:0.008650 3rd Qu.:0.012896 3rd Qu.:0.007232
## Max. :0.0180541 Max. :0.071829 Max. :0.093433 Max. :0.173909
##
## trails veg_edges water wells
## Min. :0.000e+00 Min. :0.000000 Min. :0.000e+00 Min. :0.0000000
## 1st Qu.:9.465e-05 1st Qu.:0.001437 1st Qu.:0.000e+00 1st Qu.:0.0008692
## Median :7.187e-04 Median :0.006425 Median :0.000e+00 Median :0.0068416
## Mean :1.516e-03 Mean :0.011335 Mean :1.254e-05 Mean :0.0143883
## 3rd Qu.:1.958e-03 3rd Qu.:0.015562 3rd Qu.:0.000e+00 3rd Qu.:0.0167246
## Max. :3.864e-02 Max. :0.147895 Max. :7.896e-03 Max. :0.3045854
##
## lc_class110 lc_class120 lc_class20 lc_class210
## Min. :0.00000 Min. :0.0000000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.01970 1st Qu.:0.0000000 1st Qu.:0.00000 1st Qu.:0.4607
## Median :0.03874 Median :0.0000000 Median :0.00000 Median :0.6749
## Mean :0.04838 Mean :0.0007554 Mean :0.02741 Mean :0.6324
## 3rd Qu.:0.06218 3rd Qu.:0.0000000 3rd Qu.:0.03361 3rd Qu.:0.8364
## Max. :0.73192 Max. :0.1211446 Max. :0.51965 Max. :1.0000
##
## lc_class220 lc_class230 lc_class32 lc_class33
## Min. :0.000000 Min. :0.00000 Min. :0.000e+00 Min. :0.0000000
## 1st Qu.:0.002332 1st Qu.:0.01218 1st Qu.:0.000e+00 1st Qu.:0.0000000
## Median :0.044977 Median :0.03595 Median :0.000e+00 Median :0.0000000
## Mean :0.113317 Mean :0.06341 Mean :1.748e-05 Mean :0.0046114
## 3rd Qu.:0.154669 3rd Qu.:0.08419 3rd Qu.:0.000e+00 3rd Qu.:0.0005702
## Max. :0.971773 Max. :0.72101 Max. :1.175e-02 Max. :0.3242328
##
## lc_class34 lc_class50
## Min. :0.000000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.02385
## Median :0.004043 Median :0.05717
## Mean :0.030311 Mean :0.07933
## 3rd Qu.:0.038149 3rd Qu.:0.11545
## Max. :0.557178 Max. :0.60824
## # there are some NAs in the data which will cause problems with modeling/visualization of data ignore for now but will explore these sites specifically after report
covariates_grouped <- covariates_grouped %>%
# remove rows with NAs
na.omit()Let’s look at the histograms again and see if we need to remove any features or feature groups without enough data
# use for loop to plot histograms for all covariates
for (col in 5:ncol(covariates_grouped)) {
hist(covariates_grouped[,col])
}
> IMO we don’t have enough variation in data to use the following
features/feature groups
- cfo
- Cultivation
- Reclaimed
- Recreation
- Reservoir
- Residential
- Water
- lc_class120 (aka agriculture)
- lc_class32 (aka rocks and rubble)
We also don’t have any data for following features since they don’t
plot with the hist() function
- Interchange ramp
- Landfill
- railways
So let’s modify this data and remove those features for now this step will need to be changed each year likely
covariates_grouped <- covariates_grouped %>%
# remove features we don't need
select(!c(cfo,
cultivation,
reclaimed,
recreation,
residential,
water,
lc_class120,
lc_class32,
landfill,
transfer_station,
railways))
# check that it worked
names(covariates_grouped)## [1] "array" "site" "camera"
## [4] "buff_dist" "borrowpits" "pipeline"
## [7] "clearings" "harvest" "facilities"
## [10] "mines" "roads" "seismic_lines"
## [13] "transmission_lines" "trails" "veg_edges"
## [16] "wells" "lc_class110" "lc_class20"
## [19] "lc_class210" "lc_class220" "lc_class230"
## [22] "lc_class33" "lc_class34" "lc_class50"Subset data & correlation plots
Marissa try to get the purrr code for this to work later
Now we need to subset the data for each buffer width, and then in the same loop let’s make correlation plots for these variables within each buffer
# Couldn't get this to work in purrr yet so using a loop to subset the data, create the plots, and save them all in one section... NEAT
buffer_frames <- list()
for (i in unique(covariates_grouped$buff_dist)){
print(i)
#Subset data based on radius
df<-covariates_grouped %>%
filter(buff_dist == i)
#rename dataframe on the fly
assign(paste("df", i, sep ="_"), df)
#list of dataframes
buffer_frames <-c (buffer_frames, list(df))
#Subset data based on radius
df<-covariates_grouped %>%
filter(buff_dist == i) %>%
select(where(is.numeric))
#compute a correlation matrix (watch for errors)
matrix <- cor(df)
# print and save the correlation plot on the go
# renaming for each buffer as we do
png(file.path("figures/", paste("correlation_", i, ".png")))
corrplot::corrplot(matrix,
type = 'upper',
tl.col = 'black',
title = paste0('Variable correlation plot at ', i))
dev.off()
}## [1] "250"## [1] "500"## [1] "750"## [1] "1000"## [1] "1250"## [1] "1500"## [1] "1750"## [1] "2000"## [1] "2250"## [1] "2500"## [1] "2750"## [1] "3000"## [1] "3250"## [1] "3500"## [1] "3750"## [1] "4000"## [1] "4250"## [1] "4500"## [1] "4750"## [1] "5000"# name list objects so we can extract names for plotting
buffer_frames <- buffer_frames %>%
# absurdly long way to do this but for sake of time fuck it
purrr::set_names('250 meter buffer',
'500 meter buffer',
'750 meter buffer',
'1000 meter buffer',
'1250 meter buffer',
'1500 meter buffer',
'1750 meter buffer',
'2000 meter buffer',
'2250 meter buffer',
'2500 meter buffer',
'2750 meter buffer',
'3000 meter buffer',
'3250 meter buffer',
'3500 meter buffer',
'3750 meter buffer',
'4000 meter buffer',
'4250 meter buffer',
'4500 meter buffer',
'4750 meter buffer',
'5000 meter buffer')You will get a warning about standard deviation is zero for buffer 250
Exploratory plots
add more to this section in later when we have more time to explore the covariates and choose which should be inlcuded etc.
# use this code to change figure margins otherwise will not plot because figure margines are too large
par(mar=c(1,1,1,1))
# now use purrr to plot histograms for all remaining HFI variables for each buffer
hfi_histograms <- buffer_frames %>%
purrr::imap(
~.x %>%
# filter to just the HFI variables
select(where(is.numeric) &
! starts_with('lc_class')) %>%
# pipe into hist.data.frame function to make histograms for each variable
hist.data.frame(mtitl = paste0('Histograms of HFI variables at ', .y)))
Now let’s do the same thing with the landcover variables
lc_histograms <- buffer_frames %>%
purrr::imap(
~.x %>%
# filter to just the landcover variables
select(where(is.numeric) &
starts_with('lc_class')) %>%
# pipe into hist.data.frame function to make histograms for each variable
hist.data.frame(mtitl = paste0('Histograms of landcover variables at ', .y)))
Add response metric
Now that we have the covariate data formatted we need to add the response metric (monthly proportional presence/absence) to the data frames
final_osm_2023_df <- buffer_frames %>%
purrr::map(
~.x %>%
left_join(prop_detections,
by = 'site'))Analysis
Now we are going to run a global model which includes all HFI and LC variables that at first glance (will do a more thorough check later) seem to have enough data to include as covariates for each buffer width, and then we will compare these models see which buffer width best fit the data for each species.
We don’t need to do ALL the species since many don’t have enough data.
Refer to the ACME_camera_script_9-2-2024.html or .Rmd the plot for proportional monthly detections should provide info on which species we have enough data for, can be found under Response metrics/3.Proportion monthly detections
A brief look at this fig indicates that we have enough for all the mammals in the prop_detections data frame except
- cougar
- wolverine
- caribou??? (may have enough, may not)
Black bear
ANDREW/MARISSA FIX later??
there is probably a way to shorten the following code to select particular species, I saw Andrew’s for loop in the draft script he wrote but couldn’t quite figure out how to adapt it to my purposes with the data formatted the way I have it, so I did this instead, maybe we can merge approaches later to clean this up?
Global model
Marissa SCALE MODELS
actually don’t need to since all variables are proportions already right?
Let’s start with bears and use purrr to create a global model for every buffer distance
Recall purrr::map() is magical for iterations and will
apply all the functions within the map() function to each
item of the list supplied before the the map()
function.
# create models for black bears at each buffer size
black_bear_mods <- final_osm_2023_df %>%
# use purrr map to fun the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI covariates in alphabetical order
scale(borrowpits) +
scale(pipeline) +
scale(clearings) +
scale(harvest) +
scale(facilities) +
scale(mines) +
scale(roads) +
scale(seismic_lines) +
scale(transmission_lines) +
scale(trails) +
scale(veg_edges) +
scale(wells) +
# VEG covariates in numerical order
scale(lc_class110) +
scale(lc_class20) +
scale(lc_class210) +
scale(lc_class220) +
scale(lc_class230) +
scale(lc_class33) +
scale(lc_class34) +
scale(lc_class50) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))## dropping columns from rank-deficient conditional model: scale(lc_class50)
## dropping columns from rank-deficient conditional model: scale(lc_class50)
## dropping columns from rank-deficient conditional model: scale(lc_class50)## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; function evaluation limit reached without convergence (9). See
## vignette('troubleshooting'), help('diagnose')Lots of notes about model convergence problems. Likely because there are too many variables in model and some without enough variation in data to reliably calculate and estime. We should revisit the data and thin it out a bit more
Model selection
We will use the model.sel() function from the
MuMIn package to compare the global models for each buffer
width and see which buffer fits the black bear data best
model.sel(black_bear_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(brr)) cnd(scl(clr))
## 250 meter buffer -0.7159 + 0.144600 0.05057
## 500 meter buffer -0.7088 + 0.046270 0.07333
## 750 meter buffer -0.7007 + -0.078630 0.12350
## 5000 meter buffer -0.7029 + -0.316500 0.29380
## 1500 meter buffer -0.7027 + -0.083810 0.08239
## 4000 meter buffer -0.7006 + -0.317800 0.35050
## 2000 meter buffer -0.6985 + -0.017770 0.09011
## 1250 meter buffer -0.7008 + -0.010970 0.07194
## 1750 meter buffer -0.7007 + -0.023210 0.09028
## 4250 meter buffer -0.7007 + -0.384700 0.45500
## 2250 meter buffer -0.6964 + -0.075840 0.10830
## 2500 meter buffer -0.6954 + -0.095330 0.13260
## 3750 meter buffer -0.6988 + -0.139300 0.24620
## 4750 meter buffer -0.6995 + -0.367500 0.26420
## 4500 meter buffer -0.6984 + -0.452500 0.29860
## 3500 meter buffer -0.6973 + -0.038220 0.21970
## 2750 meter buffer -0.6945 + 0.007392 0.20070
## 3250 meter buffer -0.6961 + 0.035470 0.22290
## 3000 meter buffer -0.6951 + 0.017220 0.23380
## 1000 meter buffer -0.7014 + -0.111300 0.08834
## cnd(scl(fcl)) cnd(scl(hrv)) cnd(scl(lc_c11)) cnd(scl(lc_c20))
## 250 meter buffer -0.20860 -0.003377 -0.119800 -0.11050
## 500 meter buffer -0.16850 0.004147 0.045270 -0.05206
## 750 meter buffer -0.08756 0.008714 0.009072 -0.03443
## 5000 meter buffer -0.29170 0.019870 0.069660 -0.09645
## 1500 meter buffer -0.04146 0.014790 0.034900 0.13690
## 4000 meter buffer -0.19930 0.103800 -0.043040 -0.29760
## 2000 meter buffer -0.12390 0.035990 -0.032260 -0.02103
## 1250 meter buffer -0.04526 0.002422 0.298600 0.43880
## 1750 meter buffer -0.09979 0.023800 -0.011810 0.03487
## 4250 meter buffer -0.15980 0.103500 -0.080840 -0.31420
## 2250 meter buffer -0.15580 0.042700 -0.187000 -0.32740
## 2500 meter buffer -0.15390 0.057230 -0.229900 -0.40630
## 3750 meter buffer -0.30110 0.101400 -0.120200 -0.52120
## 4750 meter buffer -0.20040 0.048850 0.045380 -0.15230
## 4500 meter buffer -0.10790 0.063170 -0.023030 -0.24550
## 3500 meter buffer -0.25790 0.104300 -0.129000 -0.46140
## 2750 meter buffer -0.16130 0.080510 -0.195200 -0.47250
## 3250 meter buffer -0.18760 0.114400 -0.172000 -0.54520
## 3000 meter buffer -0.17800 0.098270 -0.242800 -0.65260
## 1000 meter buffer -0.07708 0.004195 -1.465000 -1.65100
## cnd(scl(lc_c21)) cnd(scl(lc_c22)) cnd(scl(lc_c23))
## 250 meter buffer -0.50310 -0.2052 -0.28910
## 500 meter buffer -0.16390 0.0941 -0.14570
## 750 meter buffer 0.05084 0.2703 -0.01426
## 5000 meter buffer -0.39190 -0.1597 -0.12190
## 1500 meter buffer 0.72000 0.6888 0.19250
## 4000 meter buffer -1.07600 -0.5130 -0.30210
## 2000 meter buffer 0.35340 0.4799 0.09316
## 1250 meter buffer 1.99200 1.5630 0.64190
## 1750 meter buffer 0.41150 0.4925 0.09489
## 4250 meter buffer -1.19500 -0.6112 -0.33400
## 2250 meter buffer -0.80330 -0.3123 -0.22610
## 2500 meter buffer -1.16700 -0.5670 -0.31720
## 3750 meter buffer -1.90100 -1.0690 -0.52640
## 4750 meter buffer -0.53210 -0.2337 -0.15260
## 4500 meter buffer -0.95240 -0.5066 -0.25360
## 3500 meter buffer -1.56900 -0.8072 -0.44010
## 2750 meter buffer -1.45800 -0.7270 -0.40590
## 3250 meter buffer -1.81100 -0.9565 -0.51350
## 3000 meter buffer -2.19300 -1.2150 -0.59440
## 1000 meter buffer -6.58600 -4.1880 -2.37600
## cnd(scl(lc_c33)) cnd(scl(lc_c34)) cnd(scl(lc_c50))
## 250 meter buffer -0.146000 -0.14140
## 500 meter buffer -0.111200 0.02455
## 750 meter buffer -0.118500 0.02624
## 5000 meter buffer 0.270500 0.19920 -0.08929
## 1500 meter buffer -0.194200 -0.02358 0.17930
## 4000 meter buffer -0.192700 -0.12990 -0.29330
## 2000 meter buffer -0.172200 -0.08948 0.01919
## 1250 meter buffer -0.036700 0.28090 0.59770
## 1750 meter buffer -0.249700 -0.07943 0.04180
## 4250 meter buffer -0.095470 -0.03329 -0.30550
## 2250 meter buffer -0.291800 -0.29500 -0.29890
## 2500 meter buffer -0.420100 -0.33370 -0.37780
## 3750 meter buffer -0.410300 -0.31310 -0.54380
## 4750 meter buffer 0.115100 0.10580 -0.14860
## 4500 meter buffer 0.008272 -0.01478 -0.25560
## 3500 meter buffer -0.356500 -0.25620 -0.47190
## 2750 meter buffer -0.550100 -0.44890 -0.45930
## 3250 meter buffer -0.424900 -0.34270 -0.56400
## 3000 meter buffer -0.545500 -0.51390 -0.67120
## 1000 meter buffer -0.701400 -1.49800 -2.19700
## cnd(scl(mns)) cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns))
## 250 meter buffer 0.17730 0.06263 -0.350000 -0.06385
## 500 meter buffer 0.13490 -0.05545 -0.299700 -0.07976
## 750 meter buffer 0.14960 0.06058 -0.247300 -0.13570
## 5000 meter buffer -0.22170 -0.53620 -0.412300 -0.06155
## 1500 meter buffer 0.19620 -0.12380 0.186600 -0.18790
## 4000 meter buffer 0.01314 -0.26400 -0.219700 -0.10140
## 2000 meter buffer 0.13640 -0.25320 0.344100 -0.16260
## 1250 meter buffer 0.14800 -0.10360 -0.001055 -0.18070
## 1750 meter buffer 0.22750 -0.19500 0.294000 -0.18730
## 4250 meter buffer -0.11660 -0.18610 -0.293100 -0.09876
## 2250 meter buffer 0.15090 -0.19680 0.460700 -0.13430
## 2500 meter buffer 0.24740 -0.14860 0.486300 -0.12260
## 3750 meter buffer 0.21800 -0.19970 -0.022810 -0.08265
## 4750 meter buffer -0.18370 -0.42350 -0.194400 -0.05816
## 4500 meter buffer -0.17970 -0.27850 -0.136900 -0.07673
## 3500 meter buffer 0.16050 -0.23730 0.053710 -0.09458
## 2750 meter buffer 0.35790 -0.18170 0.396400 -0.13140
## 3250 meter buffer 0.15540 -0.25380 0.195100 -0.10090
## 3000 meter buffer 0.27180 -0.20410 0.299600 -0.11560
## 1000 meter buffer 0.08223 -0.05412 -0.015710 -0.17790
## cnd(scl(trl)) cnd(scl(trn_lns)) cnd(scl(veg_edg))
## 250 meter buffer 0.149700 -0.137100 0.026480
## 500 meter buffer 0.100600 -0.040860 0.023270
## 750 meter buffer 0.069740 -0.092940 0.012010
## 5000 meter buffer -0.020190 0.347600 0.095810
## 1500 meter buffer 0.108200 -0.015840 -0.153800
## 4000 meter buffer 0.026240 0.100600 0.020290
## 2000 meter buffer 0.095460 0.041260 -0.204200
## 1250 meter buffer 0.121100 -0.022110 -0.005932
## 1750 meter buffer 0.090890 0.014230 -0.200600
## 4250 meter buffer -0.007788 0.080290 -0.099930
## 2250 meter buffer 0.099100 -0.036780 -0.182300
## 2500 meter buffer 0.082090 -0.075010 -0.245800
## 3750 meter buffer 0.084780 0.039750 0.020450
## 4750 meter buffer 0.007153 0.227900 0.035720
## 4500 meter buffer 0.016340 0.129000 -0.055290
## 3500 meter buffer 0.086630 0.048130 -0.063410
## 2750 meter buffer 0.056150 -0.023110 -0.310900
## 3250 meter buffer 0.074220 0.040800 -0.210900
## 3000 meter buffer 0.055610 -0.000563 -0.253600
## 1000 meter buffer 0.124600 -0.040290 0.021150
## cnd(scl(wll)) df logLik AICc delta
## 250 meter buffer -0.022900 21 -301.426 651.9 0.00
## 500 meter buffer 0.080920 21 -308.069 665.1 13.29
## 750 meter buffer 0.141400 21 -309.625 668.3 16.40
## 5000 meter buffer 0.415300 22 -309.255 670.2 18.38
## 1500 meter buffer 0.122000 22 -309.876 671.5 19.63
## 4000 meter buffer 0.312900 22 -309.988 671.7 19.85
## 2000 meter buffer 0.061450 22 -310.388 672.5 20.65
## 1250 meter buffer 0.064970 22 -310.566 672.9 21.01
## 1750 meter buffer 0.076950 22 -310.571 672.9 21.01
## 4250 meter buffer 0.336400 22 -310.643 673.0 21.16
## 2250 meter buffer 0.003626 22 -311.175 674.1 22.22
## 2500 meter buffer 0.013230 22 -311.256 674.2 22.39
## 3750 meter buffer 0.078870 22 -311.489 674.7 22.85
## 4750 meter buffer 0.330600 22 -311.726 675.2 23.32
## 4500 meter buffer 0.372500 22 -311.994 675.7 23.86
## 3500 meter buffer 0.026350 22 -312.336 676.4 24.55
## 2750 meter buffer 0.074580 22 -312.422 676.6 24.72
## 3250 meter buffer -0.011380 22 -312.606 676.9 25.09
## 3000 meter buffer 0.029770 22 -312.794 677.3 25.46
## 1000 meter buffer 0.077150 22
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)hmmmm seems fishy to me that the 250 meter buffer which is the only one that had missing data would perform THAT much better than all the others, and really you shouldn’t compare models if they aren’t run on the same data, hence the warning message
Let’s remove the 250 buffer and see what happens. To do this we can
use the discard_at() function in the purr package
which will remove a list element based on it’s name (e.g. ‘250 meter
buffer’).
After that we will rerun the model selection again and see if it looks better
black_bear_mods_no250 <- black_bear_mods %>%
# purrr::discard_at will remove an item from a list
purrr::discard_at('250 meter buffer')
# run model selection again
model.sel(black_bear_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(brr)) cnd(scl(clr))
## 500 meter buffer -0.7088 + 0.046270 0.07333
## 750 meter buffer -0.7007 + -0.078630 0.12350
## 5000 meter buffer -0.7029 + -0.316500 0.29380
## 1500 meter buffer -0.7027 + -0.083810 0.08239
## 4000 meter buffer -0.7006 + -0.317800 0.35050
## 2000 meter buffer -0.6985 + -0.017770 0.09011
## 1250 meter buffer -0.7008 + -0.010970 0.07194
## 1750 meter buffer -0.7007 + -0.023210 0.09028
## 4250 meter buffer -0.7007 + -0.384700 0.45500
## 2250 meter buffer -0.6964 + -0.075840 0.10830
## 2500 meter buffer -0.6954 + -0.095330 0.13260
## 3750 meter buffer -0.6988 + -0.139300 0.24620
## 4750 meter buffer -0.6995 + -0.367500 0.26420
## 4500 meter buffer -0.6984 + -0.452500 0.29860
## 3500 meter buffer -0.6973 + -0.038220 0.21970
## 2750 meter buffer -0.6945 + 0.007392 0.20070
## 3250 meter buffer -0.6961 + 0.035470 0.22290
## 3000 meter buffer -0.6951 + 0.017220 0.23380
## 1000 meter buffer -0.7014 + -0.111300 0.08834
## cnd(scl(fcl)) cnd(scl(hrv)) cnd(scl(lc_c11)) cnd(scl(lc_c20))
## 500 meter buffer -0.16850 0.004147 0.045270 -0.05206
## 750 meter buffer -0.08756 0.008714 0.009072 -0.03443
## 5000 meter buffer -0.29170 0.019870 0.069660 -0.09645
## 1500 meter buffer -0.04146 0.014790 0.034900 0.13690
## 4000 meter buffer -0.19930 0.103800 -0.043040 -0.29760
## 2000 meter buffer -0.12390 0.035990 -0.032260 -0.02103
## 1250 meter buffer -0.04526 0.002422 0.298600 0.43880
## 1750 meter buffer -0.09979 0.023800 -0.011810 0.03487
## 4250 meter buffer -0.15980 0.103500 -0.080840 -0.31420
## 2250 meter buffer -0.15580 0.042700 -0.187000 -0.32740
## 2500 meter buffer -0.15390 0.057230 -0.229900 -0.40630
## 3750 meter buffer -0.30110 0.101400 -0.120200 -0.52120
## 4750 meter buffer -0.20040 0.048850 0.045380 -0.15230
## 4500 meter buffer -0.10790 0.063170 -0.023030 -0.24550
## 3500 meter buffer -0.25790 0.104300 -0.129000 -0.46140
## 2750 meter buffer -0.16130 0.080510 -0.195200 -0.47250
## 3250 meter buffer -0.18760 0.114400 -0.172000 -0.54520
## 3000 meter buffer -0.17800 0.098270 -0.242800 -0.65260
## 1000 meter buffer -0.07708 0.004195 -1.465000 -1.65100
## cnd(scl(lc_c21)) cnd(scl(lc_c22)) cnd(scl(lc_c23))
## 500 meter buffer -0.16390 0.0941 -0.14570
## 750 meter buffer 0.05084 0.2703 -0.01426
## 5000 meter buffer -0.39190 -0.1597 -0.12190
## 1500 meter buffer 0.72000 0.6888 0.19250
## 4000 meter buffer -1.07600 -0.5130 -0.30210
## 2000 meter buffer 0.35340 0.4799 0.09316
## 1250 meter buffer 1.99200 1.5630 0.64190
## 1750 meter buffer 0.41150 0.4925 0.09489
## 4250 meter buffer -1.19500 -0.6112 -0.33400
## 2250 meter buffer -0.80330 -0.3123 -0.22610
## 2500 meter buffer -1.16700 -0.5670 -0.31720
## 3750 meter buffer -1.90100 -1.0690 -0.52640
## 4750 meter buffer -0.53210 -0.2337 -0.15260
## 4500 meter buffer -0.95240 -0.5066 -0.25360
## 3500 meter buffer -1.56900 -0.8072 -0.44010
## 2750 meter buffer -1.45800 -0.7270 -0.40590
## 3250 meter buffer -1.81100 -0.9565 -0.51350
## 3000 meter buffer -2.19300 -1.2150 -0.59440
## 1000 meter buffer -6.58600 -4.1880 -2.37600
## cnd(scl(lc_c33)) cnd(scl(lc_c34)) cnd(scl(lc_c50))
## 500 meter buffer -0.111200 0.02455
## 750 meter buffer -0.118500 0.02624
## 5000 meter buffer 0.270500 0.19920 -0.08929
## 1500 meter buffer -0.194200 -0.02358 0.17930
## 4000 meter buffer -0.192700 -0.12990 -0.29330
## 2000 meter buffer -0.172200 -0.08948 0.01919
## 1250 meter buffer -0.036700 0.28090 0.59770
## 1750 meter buffer -0.249700 -0.07943 0.04180
## 4250 meter buffer -0.095470 -0.03329 -0.30550
## 2250 meter buffer -0.291800 -0.29500 -0.29890
## 2500 meter buffer -0.420100 -0.33370 -0.37780
## 3750 meter buffer -0.410300 -0.31310 -0.54380
## 4750 meter buffer 0.115100 0.10580 -0.14860
## 4500 meter buffer 0.008272 -0.01478 -0.25560
## 3500 meter buffer -0.356500 -0.25620 -0.47190
## 2750 meter buffer -0.550100 -0.44890 -0.45930
## 3250 meter buffer -0.424900 -0.34270 -0.56400
## 3000 meter buffer -0.545500 -0.51390 -0.67120
## 1000 meter buffer -0.701400 -1.49800 -2.19700
## cnd(scl(mns)) cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns))
## 500 meter buffer 0.13490 -0.05545 -0.299700 -0.07976
## 750 meter buffer 0.14960 0.06058 -0.247300 -0.13570
## 5000 meter buffer -0.22170 -0.53620 -0.412300 -0.06155
## 1500 meter buffer 0.19620 -0.12380 0.186600 -0.18790
## 4000 meter buffer 0.01314 -0.26400 -0.219700 -0.10140
## 2000 meter buffer 0.13640 -0.25320 0.344100 -0.16260
## 1250 meter buffer 0.14800 -0.10360 -0.001055 -0.18070
## 1750 meter buffer 0.22750 -0.19500 0.294000 -0.18730
## 4250 meter buffer -0.11660 -0.18610 -0.293100 -0.09876
## 2250 meter buffer 0.15090 -0.19680 0.460700 -0.13430
## 2500 meter buffer 0.24740 -0.14860 0.486300 -0.12260
## 3750 meter buffer 0.21800 -0.19970 -0.022810 -0.08265
## 4750 meter buffer -0.18370 -0.42350 -0.194400 -0.05816
## 4500 meter buffer -0.17970 -0.27850 -0.136900 -0.07673
## 3500 meter buffer 0.16050 -0.23730 0.053710 -0.09458
## 2750 meter buffer 0.35790 -0.18170 0.396400 -0.13140
## 3250 meter buffer 0.15540 -0.25380 0.195100 -0.10090
## 3000 meter buffer 0.27180 -0.20410 0.299600 -0.11560
## 1000 meter buffer 0.08223 -0.05412 -0.015710 -0.17790
## cnd(scl(trl)) cnd(scl(trn_lns)) cnd(scl(veg_edg))
## 500 meter buffer 0.100600 -0.040860 0.023270
## 750 meter buffer 0.069740 -0.092940 0.012010
## 5000 meter buffer -0.020190 0.347600 0.095810
## 1500 meter buffer 0.108200 -0.015840 -0.153800
## 4000 meter buffer 0.026240 0.100600 0.020290
## 2000 meter buffer 0.095460 0.041260 -0.204200
## 1250 meter buffer 0.121100 -0.022110 -0.005932
## 1750 meter buffer 0.090890 0.014230 -0.200600
## 4250 meter buffer -0.007788 0.080290 -0.099930
## 2250 meter buffer 0.099100 -0.036780 -0.182300
## 2500 meter buffer 0.082090 -0.075010 -0.245800
## 3750 meter buffer 0.084780 0.039750 0.020450
## 4750 meter buffer 0.007153 0.227900 0.035720
## 4500 meter buffer 0.016340 0.129000 -0.055290
## 3500 meter buffer 0.086630 0.048130 -0.063410
## 2750 meter buffer 0.056150 -0.023110 -0.310900
## 3250 meter buffer 0.074220 0.040800 -0.210900
## 3000 meter buffer 0.055610 -0.000563 -0.253600
## 1000 meter buffer 0.124600 -0.040290 0.021150
## cnd(scl(wll)) df logLik AICc delta
## 500 meter buffer 0.080920 21 -308.069 665.1 0.00
## 750 meter buffer 0.141400 21 -309.625 668.3 3.11
## 5000 meter buffer 0.415300 22 -309.255 670.2 5.10
## 1500 meter buffer 0.122000 22 -309.876 671.5 6.34
## 4000 meter buffer 0.312900 22 -309.988 671.7 6.56
## 2000 meter buffer 0.061450 22 -310.388 672.5 7.36
## 1250 meter buffer 0.064970 22 -310.566 672.9 7.72
## 1750 meter buffer 0.076950 22 -310.571 672.9 7.73
## 4250 meter buffer 0.336400 22 -310.643 673.0 7.87
## 2250 meter buffer 0.003626 22 -311.175 674.1 8.94
## 2500 meter buffer 0.013230 22 -311.256 674.2 9.10
## 3750 meter buffer 0.078870 22 -311.489 674.7 9.56
## 4750 meter buffer 0.330600 22 -311.726 675.2 10.04
## 4500 meter buffer 0.372500 22 -311.994 675.7 10.57
## 3500 meter buffer 0.026350 22 -312.336 676.4 11.26
## 2750 meter buffer 0.074580 22 -312.422 676.6 11.43
## 3250 meter buffer -0.011380 22 -312.606 676.9 11.80
## 3000 meter buffer 0.029770 22 -312.794 677.3 12.17
## 1000 meter buffer 0.077150 22
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)this looks much more realistic; the 500 m buffer is top model for black bears
So what we will do for each species is remove the 250 meter buffer for now since there are some data missing, and compare just the other buffer sizes that contain the full data set
Model summary 500m
Let’s take a look at the model summary for the top model
summary(black_bear_mods_no250$`500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(black_bear, absent_black_bear) ~ scale(borrowpits) + scale(pipeline) +
## scale(clearings) + scale(harvest) + scale(facilities) + scale(mines) +
## scale(roads) + scale(seismic_lines) + scale(transmission_lines) +
## scale(trails) + scale(veg_edges) + scale(wells) + scale(lc_class110) +
## scale(lc_class20) + scale(lc_class210) + scale(lc_class220) +
## scale(lc_class230) + scale(lc_class33) + scale(lc_class34) +
## scale(lc_class50) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 658.1 721.9 -308.1 616.1 133
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.01327 0.1152
## Number of obs: 154, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.708829 0.081608 -8.686 <2e-16 ***
## scale(borrowpits) 0.046269 0.064984 0.712 0.4765
## scale(pipeline) -0.055450 0.105537 -0.525 0.5993
## scale(clearings) 0.073326 0.069247 1.059 0.2896
## scale(harvest) 0.004147 0.059027 0.070 0.9440
## scale(facilities) -0.168463 0.079322 -2.124 0.0337 *
## scale(mines) 0.134867 0.078233 1.724 0.0847 .
## scale(roads) -0.299655 0.160307 -1.869 0.0616 .
## scale(seismic_lines) -0.079761 0.071753 -1.112 0.2663
## scale(transmission_lines) -0.040863 0.099056 -0.413 0.6800
## scale(trails) 0.100629 0.060398 1.666 0.0957 .
## scale(veg_edges) 0.023267 0.128452 0.181 0.8563
## scale(wells) 0.080916 0.077363 1.046 0.2956
## scale(lc_class110) 0.045271 0.079573 0.569 0.5694
## scale(lc_class20) -0.052057 0.062257 -0.836 0.4031
## scale(lc_class210) -0.163897 0.165605 -0.990 0.3223
## scale(lc_class220) 0.094103 0.125506 0.750 0.4534
## scale(lc_class230) -0.145678 0.088846 -1.640 0.1011
## scale(lc_class33) -0.111250 0.095697 -1.163 0.2450
## scale(lc_class34) 0.024545 0.112412 0.218 0.8272
## scale(lc_class50) NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Let’s repeat this process for each species that we have enough data for.
Caribou
We may or may not have enough data for caribou but let’s try it at least for this preliminary report
We can use the same code from black bears (above) to run global models for each buffer width except remember we want to remove 250 meters
And in the same chunk to save time let’s also run the
model.sel() function
Models/Model selection
caribou_mods_no250 <- final_osm_2023_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(caribou, absent_caribou) ~
# HFI covariates in alphabetical order
scale(borrowpits) +
scale(pipeline) +
scale(clearings) +
scale(harvest) +
scale(facilities) +
scale(mines) +
scale(roads) +
scale(seismic_lines) +
scale(transmission_lines) +
scale(trails) +
scale(veg_edges) +
scale(wells) +
# VEG covariates in numerical order
scale(lc_class110) +
scale(lc_class20) +
scale(lc_class210) +
scale(lc_class220) +
scale(lc_class230) +
scale(lc_class33) +
scale(lc_class34) +
scale(lc_class50) +
(1|array),
data = .,
family = 'binomial'))## dropping columns from rank-deficient conditional model: scale(lc_class50)## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')## dropping columns from rank-deficient conditional model: scale(lc_class50)## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; function evaluation limit reached without convergence (9). See
## vignette('troubleshooting'), help('diagnose')## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; function evaluation limit reached without convergence (9). See
## vignette('troubleshooting'), help('diagnose')## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): failed to invert
## Hessian from numDeriv::jacobian(), falling back to internal vcov estimate## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')# model selection
model.sel(caribou_mods_no250)## Warning in sqrt(diag(vcovs)): NaNs produced## Warning in sqrt(diag(vcovs)): NaNs produced
## Warning in sqrt(diag(vcovs)): NaNs produced## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(brr)) cnd(scl(clr))
## 3500 meter buffer -39.97 + 0.52630 -4.4770
## 3250 meter buffer -48.20 + 0.57850 -8.3020
## 4000 meter buffer -69.82 + -1.03200 -12.8700
## 4250 meter buffer -102.70 + -0.65960 -13.6100
## 4500 meter buffer -154.50 + 0.19870 -29.1700
## 5000 meter buffer -141.00 + -0.24470 -16.6200
## 4750 meter buffer -145.70 + 0.46280 -18.5600
## 2000 meter buffer -126.60 + 0.24330 -0.5072
## 1750 meter buffer -45.54 + 0.28030 -0.3973
## 1000 meter buffer -211.40 + -0.73770 -0.7295
## 1250 meter buffer -83.44 + -0.14220 -0.5565
## 750 meter buffer -57.73 + -0.36300 -3.9610
## 500 meter buffer -115.60 + -0.08530 -2.3090
## 1500 meter buffer -54.48 + -0.01644 -0.6089
## 2250 meter buffer -98.05 + 0.21490 -1.3040
## 2500 meter buffer -3726.00 + -0.61180 -6.3820
## 2750 meter buffer -113.90 + -0.60290 -9.5490
## 3000 meter buffer -87.85 + -0.27950 -8.3510
## 3750 meter buffer -76.00 + -0.43520 -8.9520
## cnd(scl(fcl)) cnd(scl(hrv)) cnd(scl(lc_c11)) cnd(scl(lc_c20))
## 3500 meter buffer 22.0800 1.30800 0.44670 0.19240
## 3250 meter buffer 20.1600 1.07000 0.28720 1.05800
## 4000 meter buffer 5.6650 1.64700 0.17720 -1.37200
## 4250 meter buffer 7.3940 0.92010 0.39600 -2.04500
## 4500 meter buffer 16.4800 -1.20000 2.91800 -5.65800
## 5000 meter buffer 15.0100 -3.23600 2.01700 -3.41500
## 4750 meter buffer 11.9500 -1.09400 2.46100 -4.47300
## 2000 meter buffer 1.7760 0.40280 -0.47450 1.62600
## 1750 meter buffer 0.5079 0.39490 -0.30720 0.89110
## 1000 meter buffer -3.0040 -0.04756 0.09140 0.21280
## 1250 meter buffer -1.6010 0.22650 -0.18520 0.23050
## 750 meter buffer -2.0090 -0.28870 -0.08214 0.30160
## 500 meter buffer -1.3450 -0.84310 -0.07359 0.13350
## 1500 meter buffer -0.6869 0.11540 0.01538 0.87390
## 2250 meter buffer 4.4380 0.57960 -0.56600 1.62500
## 2500 meter buffer 32.6600 0.44650 -0.99940 0.20600
## 2750 meter buffer 25.2700 0.23450 0.12350 2.31500
## 3000 meter buffer 22.6500 0.19820 0.35610 2.46000
## 3750 meter buffer 4.3230 2.20200 0.12080 -0.04679
## cnd(scl(lc_c21)) cnd(scl(lc_c22)) cnd(scl(lc_c23))
## 3500 meter buffer 2.4830 -1.6570 -1.2630
## 3250 meter buffer 2.5700 -3.3320 -0.1300
## 4000 meter buffer 0.5373 -0.5936 -2.6740
## 4250 meter buffer 1.0390 -1.5890 -2.0330
## 4500 meter buffer 0.5238 -3.5620 -4.2500
## 5000 meter buffer 0.6232 -4.1620 -2.8020
## 4750 meter buffer 0.5036 -3.3130 -3.5540
## 2000 meter buffer 1.9510 -4.4060 -0.3944
## 1750 meter buffer 2.0940 -4.4780 -0.0707
## 1000 meter buffer 0.7628 -1.5410 -0.1259
## 1250 meter buffer 1.2090 -2.3180 -0.1875
## 750 meter buffer 0.2055 -1.7860 -0.1655
## 500 meter buffer 0.4276 -1.8010 0.1041
## 1500 meter buffer 2.2980 -5.0590 0.1476
## 2250 meter buffer 2.2590 -1.9130 -0.4929
## 2500 meter buffer -4.6990 -9.0930 -2.7470
## 2750 meter buffer 3.0990 -3.8600 -0.4273
## 3000 meter buffer 3.6310 -5.0790 0.2340
## 3750 meter buffer 0.8903 -1.5050 -1.9120
## cnd(scl(lc_c33)) cnd(scl(lc_c34)) cnd(scl(lc_c50))
## 3500 meter buffer -42.9500 7.8150 1.4610
## 3250 meter buffer -40.7800 10.2400 1.3140
## 4000 meter buffer -3.8620 4.1570 0.8342
## 4250 meter buffer -3.8650 3.9150 0.8337
## 4500 meter buffer -1.0740 20.2100 -1.9180
## 5000 meter buffer 1.1400 14.3100 -0.8480
## 4750 meter buffer -0.5216 16.4600 -1.6960
## 2000 meter buffer -11.0900 4.9720 0.9729
## 1750 meter buffer -6.1090 4.0100 0.8697
## 1000 meter buffer 1.5910 -0.3428 0.5619
## 1250 meter buffer -0.3738 0.5782 0.6583
## 750 meter buffer 5.1470 -0.5521
## 500 meter buffer 3.8460 -0.4170
## 1500 meter buffer -2.5440 2.8520 0.8777
## 2250 meter buffer -15.3700 6.5530 1.2280
## 2500 meter buffer -76.4600 12.1600 -0.7792
## 2750 meter buffer -54.3900 11.5200 1.4620
## 3000 meter buffer -49.8600 10.5600 1.4030
## 3750 meter buffer -6.5370 3.2410 0.8534
## cnd(scl(mns)) cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns))
## 3500 meter buffer 9.38500 4.25900 -26.65000 -1.82200
## 3250 meter buffer -8.20700 3.65400 -25.15000 -1.02600
## 4000 meter buffer 14.20000 2.66000 -5.81900 0.19520
## 4250 meter buffer 12.56000 3.41500 -6.95800 -1.41400
## 4500 meter buffer -11.01000 0.84190 -18.73000 3.74900
## 5000 meter buffer -8.46600 1.68300 -10.02000 1.51900
## 4750 meter buffer -10.33000 0.02599 -11.79000 3.69300
## 2000 meter buffer 4.25000 1.21100 -7.85400 2.52500
## 1750 meter buffer 2.91100 1.32200 -6.86500 1.83000
## 1000 meter buffer -0.54750 1.50800 -0.82740 0.09458
## 1250 meter buffer -0.01695 1.47900 -3.08400 0.96880
## 750 meter buffer -2.65800 0.91230 0.05739 0.71450
## 500 meter buffer -93.67000 0.16430 0.83100 0.21080
## 1500 meter buffer 0.45280 0.92940 -3.49600 1.28900
## 2250 meter buffer -2.44500 1.26400 -11.09000 3.07400
## 2500 meter buffer -71.22000 0.58240 -14.94000 3.06300
## 2750 meter buffer -50.05000 0.51380 -12.93000 2.29900
## 3000 meter buffer -33.78000 0.24540 -11.94000 0.90010
## 3750 meter buffer 16.06000 2.56300 -5.19400 0.58190
## cnd(scl(trl)) cnd(scl(trn_lns)) cnd(scl(veg_edg))
## 3500 meter buffer -4.45900 -37.92 7.7930
## 3250 meter buffer -4.10100 -44.73 7.7910
## 4000 meter buffer -1.88400 -86.86 -1.9750
## 4250 meter buffer 0.41700 -130.30 -2.4580
## 4500 meter buffer 3.65800 -192.90 10.1000
## 5000 meter buffer 5.05700 -174.90 -5.7510
## 4750 meter buffer 2.83400 -181.80 3.7210
## 2000 meter buffer -1.27900 -209.30 2.2460
## 1750 meter buffer -1.05200 -67.55 2.0690
## 1000 meter buffer -0.10380 -419.70 0.5945
## 1250 meter buffer -0.02513 -150.50 2.3820
## 750 meter buffer -0.16810 -107.70 2.0860
## 500 meter buffer -0.11810 -230.60 1.6490
## 1500 meter buffer 0.08393 -87.14 0.4934
## 2250 meter buffer -2.36600 -150.50 3.3040
## 2500 meter buffer -3.42100 -6087.00 -1.7490
## 2750 meter buffer -2.61600 -137.50 -0.8197
## 3000 meter buffer -2.34400 -98.61 -0.2538
## 3750 meter buffer -3.35900 -99.57 -0.3062
## cnd(scl(wll)) df logLik AICc delta
## 3500 meter buffer 5.33100 22 -56.529 164.8 0.00
## 3250 meter buffer 6.19900 22 -57.095 165.9 1.13
## 4000 meter buffer -2.97800 22 -58.827 169.4 4.60
## 4250 meter buffer -2.19700 22 -59.286 170.3 5.51
## 4500 meter buffer 1.34300 22 -59.734 171.2 6.41
## 5000 meter buffer 5.31300 22 -62.553 176.8 12.05
## 4750 meter buffer 0.23400 22 -62.984 177.7 12.91
## 2000 meter buffer 0.02485 22 -69.321 190.4 25.58
## 1750 meter buffer 0.26890 22 -72.133 196.0 31.21
## 1000 meter buffer 0.70440 22 -80.257 212.2 47.46
## 1250 meter buffer -0.09405 22 -83.151 218.0 53.24
## 750 meter buffer 0.64660 21 -84.979 219.0 54.17
## 500 meter buffer 0.25930 21 -92.716 234.4 69.65
## 1500 meter buffer 0.22370 22
## 2250 meter buffer 1.13000 22
## 2500 meter buffer 5.26100 22
## 2750 meter buffer 5.09100 22
## 3000 meter buffer 5.64400 22
## 3750 meter buffer -4.14300 22
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)We get a warning that there are some model convergence problems, I expect this is because we don’t have enough data for caribou but I don’t have time to dig into this now so we will investigate more closely for final analysis
For caribou 3500m buffer is top model for now
Model summary 3500m
Let’s take a closer look at the top model summary
summary(caribou_mods_no250$`3500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(caribou, absent_caribou) ~ scale(borrowpits) + scale(pipeline) +
## scale(clearings) + scale(harvest) + scale(facilities) + scale(mines) +
## scale(roads) + scale(seismic_lines) + scale(transmission_lines) +
## scale(trails) + scale(veg_edges) + scale(wells) + scale(lc_class110) +
## scale(lc_class20) + scale(lc_class210) + scale(lc_class220) +
## scale(lc_class230) + scale(lc_class33) + scale(lc_class34) +
## scale(lc_class50) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 157.1 223.9 -56.5 113.1 132
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 3.968e-14 1.992e-07
## Number of obs: 154, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.997e+01 2.620e+03 -0.015 0.9878
## scale(borrowpits) 5.263e-01 8.465e-01 0.622 0.5341
## scale(pipeline) 4.259e+00 2.163e+00 1.969 0.0489 *
## scale(clearings) -4.477e+00 4.411e+00 -1.015 0.3101
## scale(harvest) 1.308e+00 1.313e+00 0.996 0.3191
## scale(facilities) 2.208e+01 1.044e+01 2.115 0.0344 *
## scale(mines) 9.385e+00 1.866e+01 0.503 0.6150
## scale(roads) -2.665e+01 1.188e+01 -2.243 0.0249 *
## scale(seismic_lines) -1.822e+00 2.566e+00 -0.710 0.4778
## scale(transmission_lines) -3.792e+01 3.924e+03 -0.010 0.9923
## scale(trails) -4.459e+00 2.262e+00 -1.971 0.0487 *
## scale(veg_edges) 7.793e+00 5.957e+00 1.308 0.1908
## scale(wells) 5.331e+00 6.442e+00 0.828 0.4080
## scale(lc_class110) 4.467e-01 1.340e+04 0.000 1.0000
## scale(lc_class20) 1.924e-01 2.481e+04 0.000 1.0000
## scale(lc_class210) 2.482e+00 1.155e+05 0.000 1.0000
## scale(lc_class220) -1.657e+00 7.536e+04 0.000 1.0000
## scale(lc_class230) -1.263e+00 3.057e+04 0.000 1.0000
## scale(lc_class33) -4.295e+01 8.310e+03 -0.005 0.9959
## scale(lc_class34) 7.815e+00 1.992e+04 0.000 0.9997
## scale(lc_class50) 1.461e+00 3.356e+04 0.000 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There’s nothing that catches my eye immediately as being sus about this particular model so it may not have been one with convergence issues. We will keep it in report for now
Coyote
Models/Model selection
coyote_mods_no250 <- final_osm_2023_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(coyote, absent_coyote) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(coyote_mods_no250)for coyote top model appears to be 4500 m by quite a bit
Model summary 4500m
Let’s get the model summary for this model
summary(coyote_mods_no250$`4500 meter buffer`)There is one lc class with a very high estimate and SE which seems a bit sus to me
Fisher
Models/Model selection
fisher_mods_no250 <- final_osm_2023_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(fisher, absent_fisher) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(fisher_mods_no250)For fisher top model is 2000 meter
Model summary 2000m
Let’s print the summary for this model
summary(fisher_mods_no250$`2000 meter buffer`)Again lc_class34 has a very high standard error, we may not have enough data in this landcover class to use in the final analysis
Grey wolf
Models/Model selection
wolf_mods_no250 <- final_osm_2023_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(grey_wolf, absent_grey_wolf) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(wolf_mods_no250)For grey wolf top model is 4500 m buffer
Model summary 4500m
Let’s get the model summary for this model
summary(wolf_mods_no250$`4500 meter buffer`)lc_class34 still presenting some issues, interesting that seismic lines weren’t significant and have a negative estimate
Lynx
Models/Model selection
lynx_mods_no250 <- final_osm_2023_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(lynx, absent_lynx) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(lynx_mods_no250)For lynx the top model is the 1000 m buffer
Model summary 1000m
Let’s get the model summary
summary(lynx_mods_no250$`1000 meter buffer`)Moose
Models/Model selection
moose_mods_no250 <- final_osm_2023_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(moose, absent_moose) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(moose_mods_no250)For moose the top model is the 3750 m buffer
Model summary 3750m
Let’s get the model summary
summary(moose_mods_no250$`3750 meter buffer`)Red fox
Models/Model selection
fox_mods_no250 <- final_osm_2023_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(red_fox, absent_red_fox) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(fox_mods_no250)For red fox the top model is 3750 m buffer
Model summary 3750m
Let’s get the model summary
summary(fox_mods_no250$`3750 meter buffer`)Gagh! Borrow pits does not have a reasonable estimate and SE
White-tailed deer
Models/Model selection
deer_mods_no250 <- final_osm_2023_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
# have to include the `` around the white-tailed_deer or R won't recognize it as a variable because of the -
glmmTMB::glmmTMB(cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(deer_mods_no250)For deer the top model was also the 3750 buffer
Model summary 3750m
Let’s get the model summary
summary(deer_mods_no250$`3750 meter buffer`)Coefficient plots
What we need to do now is extract the coefficient estimates from each top model, as well as the 95% CI so we can plot them for easier visualization and understanding of the data
Extract coefficients
The confint() function will extract the coefficients and
CI intervals from a model, so what we need to do is make a list of all
the models, then use the map() function in purrr
to apply the confint() function to all the models and get
the coefficients. We want this to result in a tibble that has a column
for the HFI feature (we aren’t plotting the lc_class data for this
report), the upper and lower CI, and the coefficient estimate.
In order to do this we have to do a bit of data wrangling, currently this isn’t the most pleasing way to accomplish the desired outcome, but it works.
# This is also a dog shit way to do this but I need to get this done
# make a list of the top models for each species
top_models <- list(black_bear_mods_no250$`500 meter buffer`,
caribou_mods_no250$`1250 meter buffer`,
coyote_mods_no250$`4500 meter buffer`,
fisher_mods_no250$`2000 meter buffer`,
wolf_mods_no250$`4500 meter buffer`,
lynx_mods_no250$`1000 meter buffer`,
moose_mods_no250$`3750 meter buffer`,
fox_mods_no250$`3750 meter buffer`,
deer_mods_no250$`3750 meter buffer`) %>%
# pipe into purrr to create coefficient table for all models
purrr::map(
~.x %>%
# extract the coefficients and upper and lower CI
confint() %>%
# format resulting object as a tibble data frame
as_tibble() %>%
# subset to just the HFI variables for these plots
slice_head(n = 6) %>%
# add a column where we can put the feature names
rowid_to_column() %>%
# rename the columns for plotting
rename('lower' = `2.5 %`,
'upper' = `97.5 %`,
'estimate' = Estimate,
'feature' = rowid) %>%
# rename the entries to features, need to look at the order the features are in from the model summary and ensure it matches
mutate(feature = as.factor(feature),
feature = recode(feature,
'1' = 'seismic_lines',
'2' = 'pipeline',
'3' = 'borrowpits',
'4' = 'wellsites',
'5' = 'roads',
'6' = 'trails'))) %>%
# set the names of each resulting tibble data frame to the species name
purrr::set_names('Black bear',
'Caribou',
'Coyote',
'Fisher',
'Grey wolf',
'Lynx',
'Moose',
'Red fox',
'White-tailed deer')Merge data frames
Now we have a data frame with the bet coefficients for each species, but if we want these on a plot together we need them all in one data frame.
To merge data into one data frame we can use the
list_rbind() function from the purrr package which
will take each element of the list and stack them on top of one another
just like rbind does with data frames, and if we use the names_to
argument we can extract the names of the list elements and assign them
to a column so we know which data comes from which species model (list
element)
In this code I also add a new column called uuid which contains the image id (uuid) for a phylopic silhouette of each species that I may want to use for plotting
Phylopic.or is an open source
online database of silhouettes various contributors have created for
use. There is an R package that works with this data called
rphylopic; we can use the get_uuid() function from
this package to extract the data for a silhouette for each species we
want, which is what I’ve done here.
# combine all list elements
coeffs_df_all <- list_rbind(top_models,
names_to = 'species') %>%
# change species to a factor for plotting
mutate(species = as.factor(species),
# add phylopic uuid for each species for plotting
# the uuid is extracted using getuuid with the species name as name = ''
uuid = case_when(species == 'Black bear' ~ get_uuid(name = 'Ursus americanus'),
species == 'Caribou' ~ get_uuid(name = 'Rangifer tarandus'),
species == 'Coyote' ~ get_uuid(name = 'Canis latrans'),
species == 'Fisher' ~ get_uuid(name = 'Pekania pennanti'),
species == 'Grey wolf' ~ get_uuid(name = 'Canis lupus'),
species == 'Lynx' ~ get_uuid(name = 'Lynx lynx'),
species == 'Moose' ~ get_uuid(name = 'Alces alces'),
species == 'Red fox' ~ get_uuid(name = 'Vulpes vulpes'),
species == 'White-taield deer' ~ get_uuid(name ='Odocoileus virginianus'))) Plot coefficients
Now let’s explore some different options to plot the coefficients
All species
Let’s try plotting all the species on one plot using
ggplot()
# provide data and mapping aesthetics
ggplot(coeffs_df_all, aes(x = feature,
y = estimate,
group = uuid)) +
geom_errorbar(aes(ymin = lower,
ymax = upper,
color = feature),
width = 0.4,
linewidth = 0.5,
position = position_dodge(width = 0.9)) +
# add points for each estimate for each covariate and use position = position_dodge to shift the points so all the species don't plot on top of one another
geom_phylopic(aes(x = feature,
y = estimate,
uuid = uuid),
position = position_dodge(width = 0.9),
size = 2)# combine all list elements
coeffs_df_all <- list_rbind(top_models,
names_to = 'species') %>%
# change species to a factor for plotting
mutate(species = as.factor(species),
# add phylopic uuid for each species for plotting
# the uuid is extracted using getuuid with the species name as name = ''
uuid = case_when(species == 'Black bear' ~ get_uuid(name = 'Ursus americanus'),
species == 'Caribou' ~ get_uuid(name = 'Rangifer tarandus'),
species == 'Coyote' ~ get_uuid(name = 'Canis latrans'),
species == 'Fisher' ~ '735066c6-2f3e-4f97-acb1-06f55ae075c9',
species == 'Grey wolf' ~ get_uuid(name = 'Canis lupus'),
species == 'Lynx' ~ get_uuid(name = 'Lynx lynx'),
species == 'Moose' ~ '74eab34a-498c-4614-aece-f02361874f79',
species == 'Red fox' ~ '9c977769-bf1e-44d4-82ab-f9f93dce39ca',
species == 'White-taield deer' ~ '56f6fdb2-15d0-43b5-b13f-714f2cb0f5d0')) %>%
# need to remove problematic estimate which is going to skew plot since its so large compared to others
filter(!c(species == 'Red fox' &
feature == 'wellsites'))After plotting the moose image I don’t like it, let’s manually replace it in the data
# I went on the phylopic website and saw there are three images for moose, I like the last one better so we will use it
get_uuid(name = 'Alces alces',
n = 3)
get_uuid(name = 'Odocoileus virginianus',
n = 3)
get_uuid(name = 'Pekania pennanti',
n = 2)
get_uuid(name = 'Vulpes',
n = 2)
# Then I manually copied this uuid and replaces it in the code aboveTry plotting all
ggplot(coeffs_df_all, aes(x = feature,
y = estimate,
group = uuid)) +
geom_errorbar(aes(ymin = lower,
ymax = upper,
color = feature),
width = 0.4,
linewidth = 0.5,
position = position_dodge(width = 1.2)) +
# add points for each estimate for each covariate and use position = position_dodge to shift the points so all the species don't plot on top of one another
geom_phylopic(aes(x = feature,
y = estimate,
uuid = uuid),
position = position_dodge(width = 1.2),
size = 2)ggplot(coeffs, aes(x = feature, y = estimate)) +
geom_point(size = 3, position = position_dodge(width = 0.5)) +
geom_errorbar(aes(ymin = lower, ymax = upper),
width = 0.4,
linewidth = 1,
position = position_dodge(width = 0.5)) +
geom_hline(yintercept = 0, linetype = "dashed")+
scale_color_manual(values = c("#56B4E9", "#009E73"), name = "Spatial Scale")+
theme_classic()+
ggtitle("Moose Response to Anthropogenic Disturbance Features")+
ylab("Coefficient Estimate \n \u00B1 95% CI")+
scale_x_discrete(labels =c("Borrowpits", "Harvest\nAreas", "Industrial\nSites", "Pipelines","Roads", "Seismic\nLines", "Trails", "Transmission\nLines"))+
theme(axis.title.x = element_blank(),
axis.text.x = element_text(size = 12),
axis.title.y = element_text(size = 14),
legend.title = element_text(size = 12),
plot.title = element_text(size = 15, hjust = 0.5))If all else fails can use plot_model function